Next Article in Journal
Evaluating Land Suitability and Potential Climate Change Impacts on Alfalfa (Medicago sativa) Production in Ethiopia
Next Article in Special Issue
The Role of Tropical Cyclones on the Total Precipitation in Cuba during the Hurricane Season from 1980 to 2016
Previous Article in Journal
NLOS Communication: Theory and Experiments in the Atmosphere and Underwater
Previous Article in Special Issue
Climate Perspectives in the Intra–Americas Seas
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 11
Issue 10
10.3390/atmos11101123
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identification of Tropical Cyclones’ Critical Positions Associated with Extreme Precipitation Events in Central America

by
Hugo G. Hidalgo
1,2,*,
Eric J. Alfaro
1,2,3,
Franklin HernÃandez-Castro
4,5 and
Paula M. Pérez-Briceño
1,6,7
1
Center for Geophysical Research, University of Costa Rica, San Jose 11501, Costa Rica
2
School of Physics, University of Costa Rica, San Jose 11501, Costa Rica
3
Center for Research in Marine Sciences and Limnology, University of Costa Rica, San Jose 11501, Costa Rica
4
School of Engineering in Industrial Design, Instituto Tecnológico de Costa Rica, Cartago 30101, Costa Rica
5
Horchschule für Gestaltung Schwäbisch Gmünd, Schwäbisch Gmünd D-73525, Germany
6
School of Geography, University of Costa Rica, San Jose 11501, Costa Rica
7
Hydrological Engineering, Chorotega Regional Headquarters, National University of Costa Rica, Liberia 50101, Costa Rica
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(10), 1123; https://doi.org/10.3390/atmos11101123
Submission received: 9 September 2020 / Revised: 6 October 2020 / Accepted: 9 October 2020 / Published: 19 October 2020
(This article belongs to the Special Issue Central America and Caribbean Hydrometeorology and Hydroclimate)
Browse Figures
Versions Notes

Abstract

:
Tropical cyclones are one of the most important causes of disasters in Central America. Using historical (1970–2010) tracks of cyclones in the Caribbean and Pacific basin, we identify critical path locations where these low-pressure systems cause the highest number of floods in a set of 88 precipitation stations in the region. Results show that tropical cyclones from the Caribbean and Pacific basin produce a large number of indirect impacts on the Pacific slope of the Central American isthmus. Although the direct impact of a tropical cyclone usually results in devastation in the affected region, the indirect effects are more common and sometimes equally severe. In fact, the storm does not need to be an intense hurricane to cause considerable impacts and damage. The location of even a lower intensity storm in critical positions of the oceanic basin can result in destructive indirect impacts in Central America. The identification of critical positions can be used for emergency agencies in the region to issue alerts of possible flooding and catastrophic events.

1. Introduction

Among the main transient synoptic-scale hydrometeorological hazards that affect Central America are cold fronts, easterly waves, and tropical cyclones [1,2]. The first type of hazard occurs during boreal winter, especially from December through January [3], while the other two types occur usually from middle boreal spring to middle autumn, peaking during the August-October quarter [4]. Sometimes, a particular easterly wave becomes an atmospheric disturbance that evolves into a tropical cyclone [5]. Ref. [6] also discusses other systems associated with tropical-depression wave disturbances that could be associated with tropical cyclogenesis in the tropical Atlantic and in the Eastern Tropical Pacific. These systems are characterized by troughs or segregated low-pressure systems from the Inter Tropical Convergence Zone (ITCZ).
Central America is located between two very active cyclogenetic regions, the Atlantic basin and the Eastern Tropical Pacific [4]. Tropical cyclones in these two regions can affect Central America in two different ways [7]. The first and more dramatic is the storm landfall, with important impacts associated with strong winds and heavy rains. This is called a “direct effect”. In some cases, the tropical cyclone crosses the isthmus from one basin to the other, normally from the Caribbean Sea to the Eastern Tropical Pacific [8]. A recent example is the 2016 Hurricane Otto, crossing at the Nicaraguan and Costa Rican border [9]. The second, and more recurrent influence, is called “indirect effect” associated with an induced circulation of the tropical cyclone low-pressure center over the region. In this case, the cyclone is not over the isthmus but induces a flow that interacts with the mountain chain that crosses Central America in a northwest–southeast axis that divides the isthmus in two slopes, Pacific and Caribbean. For example, a tropical cyclone located in the Caribbean Sea, close to Central America, could have the associated low-level wind response blowing from the Eastern Tropical Pacific to the Caribbean Sea. The mechanical rise of this warm and humid air originated in the Pacific, forces condensation, and can be associated with persistent rain over the Pacific slope. A recent example was Tropical Storm Nate in 2017 [10]. These events are called “temporales” if they persist for several days and can cause important stream flows [11]. Countries on the northern part of the isthmus (i.e., Belize, Guatemala, Honduras, El Salvador, and Nicaragua), are impacted by both, direct and indirect effects, meanwhile Costa Rica and Panama, at the southeast, are impacted normally by indirect effects and rarely by storm landfalls as with the tropical storm 19 in 1887 [12], Hurricane Martha in 1969 [13] and Hurricane Otto in 2016 [9]. Details from historical studies of the past impacts of Tropical Cyclones in Central America can be found in [14,15,16,17,18].
The tropical cyclone impacts registered over the region are not strictly associated with the intensity of the cyclone. There are two additional important variables to consider when the impacts databases are reviewed, the position of the system relative to the isthmus, and its velocity over the basin [18,19]. If the low pressure is located in the right position to induce a temporal for several days due to its low system velocity, it would cause important damage in the region [10]. Impacts are normally associated with extremely high stream flows or landslides and would have effects in important socioeconomic sectors like agriculture, infrastructure (both public and private), water supply for human consumption, transport, tourism, and others [1,2,20,21,22,23,24,25]. For the Central American economies, the cost of these impacts may be an important part of their gross domestic products, and sometimes they could have an associated loss of lives [22,23,24,26,26]. It is worth mentioning that impacts are also a function of the population’s exposure and vulnerability. For this reason, the local governments having important population centers living in poverty and extreme poverty, usually present high indices of vulnerability and exposure to hydrometeorological hazards. The emergency reports from these locations are among the highest when compared with the other municipalities [1,27].
As examples of the direct and indirect effects, we selected some events for each oceanic basin: Tropical Depression 16 (2008) in the Caribbean Sea; and Alma and Agatha Tropical Storm in the Eastern Tropical Pacific Ocean. The trajectories and impacts were mapped to identify the municipalities injured by these events. The impacts were extracted from two databases: DesInventar and EM-DAT. Some photographs were included to exemplify the damage.
The objective of this work is to identify the regions in the Caribbean Sea and in the Eastern Tropical Pacific, having the critical position occurrences of tropical cyclones but associated with extreme precipitation events (above the 90th percentile) in Central America, recorded by the gauge network of the national meteorological and hydrological services. This information could be useful for emergency preparedness institutions in the region, as it can help in planning and disaster prevention.

2. Data and Methodology

Daily precipitation data from 88 meteorological stations covering Central America during the period 1970–2010 were obtained from the database of the Center for Geophysical Research at the University of Costa Rica (CIGEFI-UCR in Spanish). The data originated from meteorological stations owned and administered by the region’s meteorological and hydrological agencies. Table 1 and Table 2 show metadata and statistics of the station data.
Gridded sea level pressure (SLP) and surface wind vectors from 1979 to 2010 at 0.75 by 0.75 degrees spatial resolution were obtained from the European Centre for Medium-Range Forecasts Interim Re-Analysis [28]. Daily means were based on basic synoptic hour observations. The data were used to determine the synoptic-scale atmospheric circulations related to extreme precipitation events in Central America associated with tropical cyclones.
Diverse information of the trajectories and other characteristics of tropical cyclones at hourly time steps from 1970 to 2010 were obtained from the combination of two databases: HURDAT [29] from the United States (US) National Hurricane Center and Central Pacific Hurricane Center part of the National Oceanographic and Atmospheric Administration and the final tracks from Unisys database from the repositories of the US National Aeronautics and Space Administration. The hourly data were concatenated from both data sources. The data includes the following information: hour, day, month, year, latitude, longitude, wind velocity, pressure, and instantaneous classification of the event (tropical depression, tropical storm, category 1 hurricane, category 2 hurricane, category 3 hurricane, category 4 hurricane, and category 5 hurricane). Tropical storms and hurricanes categories 1 to 5 represent named cyclones. Unless otherwise specified, extreme precipitation events (above the climatological 90th percentile) were associated with all the hourly cyclone data of that day to create composites and other subsequent analyses. The cyclone data were used as input to determine the relationship between extreme precipitation events and the locations of the tropical cyclones in the Caribbean Sea and Eastern Tropical Pacific Ocean. In Table 3, the classification of tropical cyclones according to their maximum wind speed is shown.
A visualization tool was developed in C++ (openframeworks). The selection of this platform was due to the response features that are many times superior to other programming languages such as Java or JavaScript [30]. Besides, the openframeworks platform allows the multiplatform use of the resulting application (Apple OS X, Windows, and Linux); these two parameters, efficiency and universality of use, were the most important for the selection of the programming environment of the tool. The map was georeferenced with data from Natural Earth, a public domain map dataset.
The data of meteorological events, in turn, were uploaded locally through .csv files that were prepared ad hoc to increase the efficient use of the tool. The result is a tool that is capable of handling more than 40,000 events in real-time. The tool allows the user to select a particular group of meteorological stations. Selecting the stations allows the user to visualize a composite of the trajectories of the tropical cyclones that produced extreme precipitation events (daily precipitation above the 90th percentile of the precipitation climatology of the corresponding month). The tool allows the user to select all types of tropical cyclones or only certain type(s). The regions where there is a large accumulation of critical trajectories are shown with a dense shading.
The visualization tool was used to determine the critical positions of tropical cyclones that produce extreme precipitation events in each Central American region. The results are shown separately for tropical cyclones that originated in the Eastern Pacific and Caribbean/Atlantic oceanic basins. The data from the visualization tool was used to determine the foci of critical locations that produce a large number of extreme precipitation events in Central America. Note that this analysis of the critical regions was based only on their trajectories, regardless of the cyclone intensity (the only requirement is that the maps were constructed from named storms only).
The regions that produce extreme weather in Central America were identified from the trajectory density maps. For each critical region (where the density of trajectories was higher), an index was produced to analyze the associated atmospheric patterns and the time evolution of the presence of storms (of different ranges of categories) in the critical zones.
Contour maps of the number of stations in extreme precipitation events were plotted at the median daily position of the cyclone. This was used to identify critical cyclone locations associated with the largest quantity of impacts. To obtain a better geovisualization, the data were improved in a Geographic Information System Software (GISS) like ArcMap 10.7 from ArcGIS UCR license.

3. Results and Discussion

3.1. Caribbean Oceanic Basin Results

The density maps of the trajectories of hurricanes categories 1 to 5 in the Caribbean basin, associated with extreme precipitation events in the countries of Central America are shown in Figure 1. Although it is evident that hurricanes can produce impacts when they are located anywhere around the Caribbean basin, there are indications of several poles of the high density of trajectories associated with extreme precipitation events. To illustrate the atmospheric circulation associated with one pole of high accumulation of trajectories located off the Yucatan Peninsula and the Gulf of Honduras (which is present in several of the country maps), we produced SLP and wind composites during extreme precipitation events concurrent to days when the hurricanes were located in that pole (positions inside the box bounded by 14.5 ∘ N to 23 ∘ N and 84 ∘ W to 90 ∘ W) (Figure 2). As expected, there is a low-pressure anomaly near the composited average location of the hurricanes inside the box. The low-pressure system implies a gradient between the pressure anomalies in the Pacific and the Caribbean, and this configuration drives anomalous winds from the Pacific Ocean to the west coast, which causes indirect impacts by orographic lifting in the slope opposite to the location of the hurricane. This type of hurricane impact is well known to the emergency response authorities in the Central American countries and mentioned in impact studies literature [1], but here a better identification of critical zones will be provided below. The mechanism was also previously illustrated in the schematic shown in Figure 3 of [7], but here we used actual data for analyzing the circulation patterns.
Two aspects are interesting to note from Figure 2: first, the coloring of the meteorological stations evidences the relative difference in the number of extreme precipitation events on both slopes of the isthmus (also consistent with the difference of impacts in both slopes reported by [1]). That is, the largest number of extreme precipitation events are reported on the Pacific slope of Nicaragua, a moderate number on the Pacific slope of Guatemala and on the Pacific northwestern coast of Costa Rica with fewer reports observed in the Caribbean slopes, southern Costa Rica, and Panama. This is interesting, as it confirms that the atmospheric circulation patterns of Figure 2 are consistent with the precipitation response and that emergency response resources associated with hurricane indirect effects in Central America should be focused on the Pacific slope. Indirect effects are an important threat in southern Central American countries, as a direct hit of a hurricane in these countries is rare considering the normal patterns of Caribbean cyclone tracks. Colon (1969) cited by [18], determined that the probability of a Caribbean hurricane landing in Costa Rica is less than 5 %. Along with indirect effects, northern countries experience occasional direct hits of hurricanes from the Caribbean, and several large events (e.g., hurricanes Mitch and Stan) have resulted in catastrophes of large proportions. The second issue that is interesting to note is that although in Figure 1 the analyzed pole of higher track density is located in the Gulf of Honduras, the SLP pattern depicted in Figure 2a is located further north within the interest box, just offshore the Yucatan Peninsula. A possible reason is that Figure 1 does not take into consideration the hurricane intensity, while in Figure 2a the composite is constructed by averaging the SLP anomalies during days of extreme precipitation events, and therefore the larger SLP anomalies from stronger hurricanes dominate the composite. This may result in the difference in the critical hurricane position shown. Nevertheless, trajectories that end up in the critical box identified before, may result in significant indirect impacts on the Pacific slope of the Central American countries. This is an important observation, as the forecasts of storm trajectories (regardless of the intensity of the cyclone) that may contain this zone, should be considered for issuing suitable emergency alerts. The use of the software tool mentioned before can be used in an interactive manner to determine possible critical regions, associated with extreme precipitation events in one or more user-selected meteorological stations. This tool is a valuable asset for emergency response institutions in the region.
In Figure 2b the streamlines associated with extreme precipitation events in each of the countries combined with critical hurricane positions in the high-density pole of interest are shown. As can be seen, there is cyclonic circulation near the position of low pressure shown in Figure 2a that draws wind and moisture from the Pacific, especially affecting Nicaragua.
We counted the number of trajectories from 1948 to 2010 reaching the critical zone defined before. The results are shown in Figure 3. As can be seen, although the trends are positive in all cases, none of them are significant at the 95 % confidence level according to the Mann-Kendall test [31,32] applied to the data, in agreement with the previous results of [16,17,33], and partially with the results of [34].
Note that although there are extreme precipitation events in the Caribbean coast near the low-pressure critical zone, probably associated with the direct impacts of the hurricanes, the number of stations reporting extreme precipitation events (red dots in Figure 2) is greater on the Pacific slope, where the effects are indirect. The direct hit of a hurricane usually brings destruction and a large number of impacts to the affected region, but the indirect effects are more frequently reported in the precipitation stations of the Pacific slope. The prevalence of impacts in this subregion is also shown in the spatial distribution of tropical cyclone impacts analyzed by [1]. Another important issue is that even a relatively lower intensity storm located in a critical position can result in a catastrophe. For example, that was the case of Tropical Depression 16 of 2008, an unnamed storm that caused a disaster on the Costa Rican Pacific slope [35]. Around 490 communities were affected by flooding, landslides, and infrastructure damages [35]. In Costa Rica, there were around 1850 people directly affected by flooding and landslides and approximately 92,600 people, directly and indirectly, impacted [35]. The trajectory and municipalities impacted by the storm are shown in Figure in the Appendix A Figure A1.
In Figure 4, we identified the critical cyclone locations that produce the largest number of stations reporting extreme precipitation events. One of the most consistent critical regions in the different classification of events used in Figure 4 is the zone of the Gulf of Honduras. It is evident that the location of the cyclones in this position directly affects the Caribbean coast and a large number of indirect effects in the stations of the Pacific slope.

3.2. Eastern Tropical Pacific Oceanic Basin Results

In the Eastern Tropical Pacific Ocean, the spatial distribution of trajectories associated with heavy rainfall in Central America is scattered in a large region (Figure 5). In a similar fashion as for the Caribbean, we selected a critical region of high trajectory density. In this case the box defined by 10 ∘ N to 25 ∘ N and 101 ∘ W to 121 ∘ W was selected for studying the associated circulation, which is shown in Figure 6. As can be seen, the box is now significantly larger and contains a large number of dispersed events. The contours of Figure 6a are more closely plotted, but even so, the low-pressure anomaly is significantly weaker than for the Caribbean. The streamlines in the Pacific are widely spaced and there is a vortex in the Caribbean associated with curled low velocities, that may be marginally affecting the Pacific moisture carrying mechanism pattern.
In Figure 7 the locations of the critical zones are identified. As can be seen, there are a lot of dispersed locations in the Pacific that cause extreme precipitation events, but in a smaller number of stations with respect to the Caribbean. The exception is when lower intensity cyclones (Figure 7) are located near and on the coast of Nicaragua and Costa Rica, for example, when the Tropical Storm Alma landed in Nicaragua in 2008. However, comparing Figure 7 to Figure 4 it is evident that these types of cyclone locations are rare. Another important issue is that as in the Atlantic basin, even a relatively lower intensity storm located in a critical position at the Eastern Tropical Pacific can induce a circulation over Central America that could result in important impacts, like the low-pressure system located offshore Guanacaste and causing damage on the Costa Rican slope from 22 to 28 May at this early cyclone stage. This system evolved later into Tropical Storm Agatha [36]. The trajectory and municipalities impacted by Tropical Storm Alma and Tropical Storm Agatha are shown in Figure A2.
The number of storms in the Pacific’s critical zone appear to be decreasing from 1970 to 2010 (Figure 8).The trends are significant when all cyclones and only named cyclones are considered. In general, the impacts of Pacific oceanic basin cyclones are also located on the Pacific slope of the isthmus. These results are in partial agreement with those of [34,37], who found no evidence of positive trends in the annual time series of Tropical Cyclone occurrences in the Eastern Tropical Pacific basin.

4. Conclusions

Tropical cyclones from the Caribbean and Pacific basin produce a large number of indirect impacts on the Pacific slope of the Central American isthmus. Although the direct impact of a tropical cyclone usually results in devastation in the affected region, the indirect effects are more common and sometimes equally severe. In fact, the storm does not need to be an intense hurricane to cause considerable impacts and damage. The location of even a lower intensity storm in critical positions of the oceanic basin can result in destructive indirect impacts in Central America.
Storms’ critical locations in the Caribbean oceanic basin are identified with more precision compared to the Pacific basin. One important critical zone is located in the Gulf of Honduras and surrounding areas. The Pacific basin location is more widespread, and the largest number of extreme precipitation events were observed when the cyclone was located off the Pacific coast of Nicaragua and Costa Rica and inside the continent (i.e., Tropical Storm Alma in 2008). For other positions, the number of impacted stations is smaller, and the locations of critical regions are not very well defined.
Positive (but not significant) trends (1948–2010) in the number of incursions of tropical cyclones in the Caribbean critical region were observed. There is great variability from year to year in these data. In the Pacific, a larger area of influence showed significant decreasing trends (1970–2010) when summing the incursions of all cyclones and when only named storms were considered. Although the influence of low-frequency climate modes can not be discarded, attention should be given by the authorities to the possible increase of indirect effects from Caribbean cyclones entering critical zones.
Identifying these critical locations is important for emergency preparedness. Forecasts issued that contain cyclones’ projected trajectories into those critical zones must result in adequate alerts in potentially affected communities. The information from this work will be shared with the emergency agencies of the region.

Author Contributions

Conceptualization, H.G.H., and E.J.A.; methodology, H.G.H., E.J.A. and F.H.-C.; software, F.H.-C., H.G.H. and P.M.P.-B.; validation, H.G.H., E.J.A. F.H.-C.; formal analysis, H.G.H. and E.J.A.; investigation, H.G.H., E.J.A. F.H.-C. and P.M.P.-B.; resources, H.G.H. and E.J.A.; data curation, H.G.H., F.H.-C. and P.M.P.-B.; writing–original draft preparation, H.G.H., E.J.A. and P.M.P.-B.; writing–review and editing, H.G.H., E.J.A., P.M.P.-B. and F.H.-C.; visualization, F.H.-C., P.M.P.-B. and H.G.H.; supervision, H.G.H. and E.J.A.; project administration, H.G.H., E.J.A.; funding acquisition, H.G.H. and E.J.A. All authors have read and agreed to the published version of the manuscript.

Funding

The authors wish to acknowledge the funding of this research to the projects B8-766 (VI-Redes), B9-454 (VI-Grupos), EC-497 (FEES-CONARE), C0-074, and C0-610 (Fondo de Estímulo), from the Center for Geophysical Research (CIGEFI) of the UCR.

Acknowledgments

To the UCR School of Physics for giving us the research time to develop this study. To the UCR research center CIGEFI for their logistic support during the data compilation and analysis. To the Central American NW & HSs for providing the rain gauge data used in this work and in the construction of the CIGEFI NUMEROSA database. Thanks to Jasson Mora Mussio for assistance in geospatial processing and to Jeaustin Sirias Chacón for his help to format this manuscript. Thanks to Dr. Ana María Durán-Quesada, Dr. Luis Gimeno (Invited Editors) for the invitation to participate in this Special Issue and Mr. Andrei-Cosmin Diaconu (Assistant Editor) for his help handling this article. Thanks to two anonymous reviewers who improved the quality of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Atmosphere 11 01123 g0a1 550
Figure A1. Examples of tropical cyclones in the Caribbean that caused widespread direct and indirect impacts.
Figure A1. Examples of tropical cyclones in the Caribbean that caused widespread direct and indirect impacts.
Atmosphere 11 01123 g0a1
Atmosphere 11 01123 g0a2 550
Figure A2. Same as Figure A1, but for the Pacific.
Figure A2. Same as Figure A1, but for the Pacific.
Atmosphere 11 01123 g0a2

References

  1. Pérez-Briceño, P.; Alfaro, E.; Hidalgo, H.; Jiménez, F. Distribución espacial de impactos de eventos hidrometeorológicos en América Central. Rev. Climatol. 2016, 16, 63–75. [Google Scholar]
  2. Alfaro, E.; Pérez-Briceño, P.M. Análisis del impacto de fenómenos meteorológicos en Costa Rica, América Central, originados en los mares circundantes. Rev. Climatol. 2014, 14, 1–11. [Google Scholar]
  3. Zárate-Hernández, E. Climatología de masas invernales de aire frío que alcanzan Centroamérica y el Caribe y su relación con algunos índices Árticos. Tópicos Meteorológicos y Oceanográficos 2013, 12, 35–55. [Google Scholar]
  4. Amador, J.; Durán-Quesada, A.M.; Mora, E.R.R.G.; Sáenz, F.; Calderón, B.; Mora, N. The easternmost tropical Pacific. Part II: Seasonal and intraseasonal modes of atmospheric variability. Rev. Biol. Trop. 2016, 64, 23–57. [Google Scholar] [CrossRef]
  5. Galvin, J.F.P. The weather and climate of the tropics: Part 7—Tropical revolving storms. Weather 2008, 63, 327–333. [Google Scholar] [CrossRef]
  6. Serra, Y.L.; Jiang, X.; Tian, B.; Amador-Astua, J.; Maloney, E.D.; Kiladis, G.N. Tropical Intraseasonal Modes of the Atmosphere. Annu. Rev. Environ. Resour. 2014, 39, 189–215. [Google Scholar] [CrossRef]
  7. Peña, M.; Douglas, M.W. Characteristics of Wet and Dry Spells over the Pacific side Central America during the rainy season. Mon. Weather Rev. 2002, 130, 3054–3073. [Google Scholar] [CrossRef]
  8. Banichevich, A.; Lizano, O. Interconexiones a nivel ciclónico-atmosférico entre las vertientes del Caribe y el Pacífico Centroamericano. Rev. Biol. Trop. 2016, 46, 8–21. [Google Scholar]
  9. Quesada-Román, A.; Fallas-López, B.; Hernández-Espinoza, K.; Stoffel, M.; Ballesteros-Cánovas, J.A. Relationships between earthquakes, hurricanes, and landslides in Costa Rica. Landslides 2019, 16, 1539–1550. [Google Scholar] [CrossRef]
  10. Alfaro, E.J.; Hidalgo, H.G.; Maldonado, T.; Pérez-Briceño, P.M.; Mora, N.P. A Tri-dimensional Approach to Climate Sciences. Caribb. Q. 2018, 64, 26–56. [Google Scholar] [CrossRef]
  11. Fallas, J.C.; Oviedo, R. Fenómenos Atmosféricos y Cambio Climático, Visión Centroamericana: “Temporales”. Cap. III; Instituto Meteorológico Nacional: San José, Costa Rica, 2003; Volume 38. [Google Scholar]
  12. Díaz, R. La tormenta tropical de 1887 y su paso por las fuentes históricas costarricenses. Rev. Estud. 2004, 18, 39–56. [Google Scholar]
  13. Simpson, R.; Sugg, A.; Clark, G.; Frank, N.; Hebert, P.; Craft, R.; Pelissier, J. The Atlantic Hurricane season of 1969. Mon. Weather Rev. 1970, 98, 293–306. [Google Scholar] [CrossRef] [Green Version]
  14. Díaz, R.; Alfaro, E. El impacto social de los Ciclones Tropicales en América Central durante el siglo XX a través del análisis de fuentes hemerográficas. Tópicos Meteorológicos y Oceanográficos 2015, 14, 12–38. [Google Scholar]
  15. Chaves, F.S.; MartíNez, E.A.; BolañOs, R.D. Análisis del impacto de los ciclones tropicales en la Cuenca del Caribe según fuentes hemerográficas (1870-2007). Revista Tópicos Meteorológicos y Oceanográficos 2015, 14, 46–75. [Google Scholar]
  16. Alfaro, E.; Quesada, A. Ocurrencia de ciclones tropicales en el Mar Caribe y sus impactos sobre Centroamérica. Rev. Intersedes 2010, 11, 136–153. [Google Scholar]
  17. Alfaro, E.; Quesada, A.; Solano, F. Análisis del impacto en Costa Rica de los ciclones tropicales ocurridos en el Mar Caribe desde 1968 al 2007. Diálogos 2010, 11, 22–38. [Google Scholar]
  18. Alvarado, L.; Alfaro, E. Frecuencia de los ciclones tropicales que afectaron a Costa Rica durante el siglo XX. Tópicos Meteorológico y Oceanográficos 2003, 10, 1–11. [Google Scholar]
  19. Taylor, M.; Alfaro, E. Climate of Central America and the Caribbean. Encycl. World Climatol. 2005, 14, 183–189. [Google Scholar]
  20. Amador, J.A.; Hidalgo, H.G.; Alfaro, E.J.; Calderón, B.; Mora, N. State of the Climate in 2019. Bull. Am. Meteorol. Soc. 2020, 101, S337–S339. [Google Scholar] [CrossRef]
  21. Amador, J.A.; Hidalgo, H.G.; Alfaro, E.J.; Calderón, B.; Mora, N. State of the Climate in 2018. Bull. Am. Meteorol. Soc. 2019, 100, S197–S198. [Google Scholar] [CrossRef] [Green Version]
  22. Amador, J.A.; Hidalgo, H.G.; Alfaro, E.J.; Calderón, B.; Mora, N. State of the Climate in 2017. Bull. Am. Meteorol. Soc. 2018, 99, S99–S200. [Google Scholar] [CrossRef]
  23. Amador, J.A.; Hidalgo, H.G.; Alfaro, E.J.; Durán-Quesada, A.M.; Calderón, N.M.B.; Arce, D. State of the Climate in 2016. Bull. Am. Meteorol. Soc. 2017, 98, S180–S183. [Google Scholar] [CrossRef] [Green Version]
  24. Campos-Durán, D.; Quesada-Román, A. Intensive and Extensive Risk in Central America Between 1990 and 2015. Anuário do Instituto de Geociências UFRJ 2017, 40, 234–249. [Google Scholar] [CrossRef]
  25. Nastos, P.; Papadimou, K.K.; Matsangouras, I. Mediterranean tropical-like cyclones: Impacts and composite daily means and anomalies of synoptic patterns. Atmos. Res. 2018, 208, 156–166. [Google Scholar] [CrossRef]
  26. Adamson-Bonilla, M. Costa Rica en el Tercer Milenio: Desafíos y Propuestas para la Reducción de la Vulnerabilidad ante Desastres; Universidad de Costa Rica: San José, Costa Rica, 2012; pp. 25–52. [Google Scholar]
  27. Quesada-Román, A.; Villalobos-Portilla, E.; Campos-Durán, D. Hydrometeorological Disasters in Urban Areas of Costa Rica, Central America. Environ. Hazards 2020, 1–15. [Google Scholar] [CrossRef]
  28. Dee, D.P.; Uppala, S.M.; Simmons, A.J.; Berrisford, P.; Poli, P.; Kobayashi, S.; Andrae, U.; Balmaseda, M.A.; Balsamo, G.; Bauer, P.; et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 2011, 137, 553–597. [Google Scholar] [CrossRef]
  29. Landsea, C.W.; Franklin, J.L. Atlantic Hurricane Database Uncertainty and Presentation of a New Database Format. Mon. Weather Rev. 2013, 141, 3576–3592. [Google Scholar] [CrossRef]
  30. Hernández-Castro, F.; Monge-Fallas, J. Eficiencia comparativa en animaciones en javascript (nota técnica). Tecnología en Marcha 2018, 31, 143–150. [Google Scholar] [CrossRef]
  31. Mann, H. Non-parametric tests against trend. Econometrica 1945, 13, 163–171. [Google Scholar] [CrossRef]
  32. Kendall, M.G. Rank Correlation Methods, 4th ed.; Griffin: London, UK, 1975. [Google Scholar]
  33. Alfaro, E. Escenarios climáticos para temporadas con alto y bajo número de huracanes en el Atlántico. Rev. Climatol. 2007, 7, 1–13. [Google Scholar]
  34. Amador, J.; Rivera, E.R.; Durán-Quesada, A.M.; Mora, G.; Sáenz, F.; Calderón, B.; Mora, N. The easternmost tropical Pacific. Part I: A climate review. Revista de Biología Tropical 2016, 64, 1–22. [Google Scholar] [CrossRef] [Green Version]
  35. Chinchilla, G. XXXIII Boletín Meteorológico: Comentario Meteorológico de Octubre de 2008; Instituto Meteorológico Nacional: San Jose, Costa Rica, 2008. [Google Scholar]
  36. Chinchilla, G.; Quirós, E.; Stolz, W. Boletín Meteorológico: Resumen Meteorológico Mayo de 2010; Instituto Meteorológico Nacional: San Jose, Costa Rica, 2010. [Google Scholar]
  37. Alfaro, E. Variación de la frecuencia de los ciclones tropicales en el Atlántico y Pacífico Tropical del Este debido a variaciones decadales e interanuales de la Temperatura Superficial del Mar. In Memorias del XI Congreso Latinoamericano e Ibérico de Meteorología y XIV Congreso Mexicano de Meteorología; Federacion Latinoamericana e Iberica de Sociedades de Meteorologia and Organización Mexicana de Meteorólogos: Cancún, Mexico, 2005. [Google Scholar]
Atmosphere 11 01123 g001 550
Figure 1. Densities of Caribbean tropical cyclones trajectories that produce extreme precipitation events in countries of Central America (data 1970–2010). The shades are shown with an arbitrary intensity scale (only their relative spatial pattern is of interest within each of the maps). The scale of the shading represents the density of red color shades of the intersection from 1 to 15 trajectories. Gray dots are the location of the inactive stations, light green dots are the stations active in the tool that are associated with the red cyclone densities shadings. The subfigures correspond to the densities of tropical cyclones trajectories associated with extreme precipitation events in the meteorological stations of the different Central America countries shown with green dots in the figure. The subfigures are shown for El Salvador (a), Nicaragua (b), Belize (c), Panama (d), Guatemala (e), Costa Rica (f) and Honduras (g). Data 1970–2010.
Figure 1. Densities of Caribbean tropical cyclones trajectories that produce extreme precipitation events in countries of Central America (data 1970–2010). The shades are shown with an arbitrary intensity scale (only their relative spatial pattern is of interest within each of the maps). The scale of the shading represents the density of red color shades of the intersection from 1 to 15 trajectories. Gray dots are the location of the inactive stations, light green dots are the stations active in the tool that are associated with the red cyclone densities shadings. The subfigures correspond to the densities of tropical cyclones trajectories associated with extreme precipitation events in the meteorological stations of the different Central America countries shown with green dots in the figure. The subfigures are shown for El Salvador (a), Nicaragua (b), Belize (c), Panama (d), Guatemala (e), Costa Rica (f) and Honduras (g). Data 1970–2010.
Atmosphere 11 01123 g001
Atmosphere 11 01123 g002 550
Figure 2. Average anomalies of sea level pressure (SLP) (a) and wind vectors with associated streamlines (b) during extreme precipitation events in Central America meteorological stations and when the hurricanes occupied the region bounded by 14.5 ∘ N to 23 ∘ N and 84 ∘ W to 90 ∘ W. Cold (warm) colors of the land meteorological precipitation stations denote a large (small) number of registered extreme precipitation events during the period that produces the composite. SLP contours every 2 hPa (thin lines) and every 10 hPa (thick lines). Although the trajectories data cover the period 1970 to 2010, this illustrative figure was constructed using data from 1979 to 2010 due to limitations in the availability of SLP data.
Figure 2. Average anomalies of sea level pressure (SLP) (a) and wind vectors with associated streamlines (b) during extreme precipitation events in Central America meteorological stations and when the hurricanes occupied the region bounded by 14.5 ∘ N to 23 ∘ N and 84 ∘ W to 90 ∘ W. Cold (warm) colors of the land meteorological precipitation stations denote a large (small) number of registered extreme precipitation events during the period that produces the composite. SLP contours every 2 hPa (thin lines) and every 10 hPa (thick lines). Although the trajectories data cover the period 1970 to 2010, this illustrative figure was constructed using data from 1979 to 2010 due to limitations in the availability of SLP data.
Atmosphere 11 01123 g002
Atmosphere 11 01123 g003 550
Figure 3. Annual number of tropical cyclones in the region bounded by 14.5 ∘ N to 23 ∘ N and 84 ∘ W to 90 ∘ W. Named cyclones refer to tropical storms and hurricanes category 1 to 5. All hurricanes refer to hurricanes categories 1 to 5. Intense hurricanes are those reaching category 3 to 5. Each subfigure correspond to the number of trajectories for all cyclones The red lines are the number of trajectories en the selected region and the dashed black line is the trend. Non-significant trends at the 95% confidence level are indicated with N.S.. Data 1948–2010. (a) All cyclones, (b) named cyclones, (c) all hurricanes, (d) intense hurricanes.
Figure 3. Annual number of tropical cyclones in the region bounded by 14.5 ∘ N to 23 ∘ N and 84 ∘ W to 90 ∘ W. Named cyclones refer to tropical storms and hurricanes category 1 to 5. All hurricanes refer to hurricanes categories 1 to 5. Intense hurricanes are those reaching category 3 to 5. Each subfigure correspond to the number of trajectories for all cyclones The red lines are the number of trajectories en the selected region and the dashed black line is the trend. Non-significant trends at the 95% confidence level are indicated with N.S.. Data 1948–2010. (a) All cyclones, (b) named cyclones, (c) all hurricanes, (d) intense hurricanes.
Atmosphere 11 01123 g003
Atmosphere 11 01123 g004 550
Figure 4. Contours depicting the number of meteorological stations in Central America reporting extreme precipitation events (above 90th percentile) plotted in the median corresponding daily location of the Caribbean cyclones. The category of the cyclone was determined from the median category identifier number of the hourly data of each day. Data from 1970 to 2010. (upper left) All cyclones, (upper right) named cyclones, (lower left) all hurricanes, (lower right) intense hurricanes.
Figure 4. Contours depicting the number of meteorological stations in Central America reporting extreme precipitation events (above 90th percentile) plotted in the median corresponding daily location of the Caribbean cyclones. The category of the cyclone was determined from the median category identifier number of the hourly data of each day. Data from 1970 to 2010. (upper left) All cyclones, (upper right) named cyclones, (lower left) all hurricanes, (lower right) intense hurricanes.
Atmosphere 11 01123 g004
Atmosphere 11 01123 g005 550
Figure 5. Same as Figure 1, but for cyclones in the Eastern Tropical Pacific basin. The subfigures are shown for El Salvador (a), Nicaragua (b), Belize (c), Panama (d), Guatemala (e), Costa Rica (f) and Honduras (g).
Figure 5. Same as Figure 1, but for cyclones in the Eastern Tropical Pacific basin. The subfigures are shown for El Salvador (a), Nicaragua (b), Belize (c), Panama (d), Guatemala (e), Costa Rica (f) and Honduras (g).
Atmosphere 11 01123 g005
Atmosphere 11 01123 g006 550
Figure 6. Same as Figure 2 but for anomalies of sea level pressure (SLP) (a) and wind vectors with associated streamlines (b) for a Pacific Ocean box 10 ∘ N to 25 ∘ N and 101 ∘ W to 121 ∘ W. SLP contours every 1 hPa (thin lines) and every 3 hPa (thick lines).
Figure 6. Same as Figure 2 but for anomalies of sea level pressure (SLP) (a) and wind vectors with associated streamlines (b) for a Pacific Ocean box 10 ∘ N to 25 ∘ N and 101 ∘ W to 121 ∘ W. SLP contours every 1 hPa (thin lines) and every 3 hPa (thick lines).
Atmosphere 11 01123 g006
Atmosphere 11 01123 g007 550
Figure 7. Same as Figure 4, but for Eastern Tropical Pacific ocean cyclones.(upper left) All cyclones, (upper right) named cyclones, (lower left) all hurricanes, (lower right) intense hurricanes.
Figure 7. Same as Figure 4, but for Eastern Tropical Pacific ocean cyclones.(upper left) All cyclones, (upper right) named cyclones, (lower left) all hurricanes, (lower right) intense hurricanes.
Atmosphere 11 01123 g007
Atmosphere 11 01123 g008 550
Figure 8. Same as Figure 3, but for a Pacific Ocean box 10 ∘ N to 25 ∘ N and 101 ∘ W to 121 ∘ W. (a) All cyclones, (b) named cyclones, (c) all hurricanes, (d) intense hurricanes.
Figure 8. Same as Figure 3, but for a Pacific Ocean box 10 ∘ N to 25 ∘ N and 101 ∘ W to 121 ∘ W. (a) All cyclones, (b) named cyclones, (c) all hurricanes, (d) intense hurricanes.
Atmosphere 11 01123 g008
Table
Table 1. Station metadata and median value [mm/day] by month.
Table 1. Station metadata and median value [mm/day] by month.
No.CountryStation Name/CodeLatitudeLongitudeJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
1BelicePswgia17.500−88.3000.000.000.000.000.000.401.000.901.301.000.300.10
2Costa RicaBagaces10.533−85.2500.000.000.000.000.000.600.100.403.001.490.000.00
3Costa RicaBatan10.083−83.3331.000.600.100.301.201.503.501.500.300.502.002.10
4Costa RicaCatie9.883−83.6330.400.100.000.102.103.303.503.603.102.802.801.40
5Costa RicaCiudad_Quesada10.333−84.4330.200.000.000.002.607.307.407.509.007.504.901.90
6Costa RicaCoto 478.602−82.9680.000.000.000.505.595.105.808.0010.2010.205.900.00
7Costa RicaEl Carmen10.200−83.4801.500.800.200.501.702.906.003.200.601.403.303.20
8Costa RicaFabio Baudrit10.000−84.2500.000.000.000.001.903.001.002.305.405.000.200.00
9Costa RicaJilguero10.450−84.7176.403.751.901.517.6012.2015.4013.1010.8512.7011.9510.45
10Costa RicaJuan Sria10.000−84.2170.000.000.000.002.402.900.802.305.554.700.200.00
11Costa RicaLa Argentina10.033−84.3500.000.000.000.002.072.901.203.207.505.800.000.00
12Costa RicaLa Lola10.100−83.3801.300.600.100.301.502.304.102.300.500.902.803.00
13Costa RicaLiberia10.599−85.5410.000.000.000.000.000.800.000.504.202.600.000.00
14Costa RicaLimón9.950−83.0171.801.100.400.701.201.704.701.700.200.602.052.90
15Costa RicaLinda Vista del Guarco
+Lankester CIGEFI		9.850−83.9000.000.000.000.001.303.101.302.606.104.200.300.00
16Costa RicaLinda_Vista9.933−84.0830.000.000.000.000.001.200.000.502.452.300.000.00
17Costa RicaLlorona9.400−84.0830.000.000.000.003.603.804.045.607.608.004.200.00
18Costa RicaMonteverde10.136−84.1920.000.000.000.004.207.304.706.2010.208.102.800.40
19Costa RicaMonteverde+
Monteverde CIGEFI		10.133−84.8250.700.400.200.102.004.203.404.906.706.102.451.40
20Costa RicaNicoya10.150−85.4500.000.000.000.000.102.000.101.706.803.700.000.00
21Costa RicaPalo_Seco9.433−84.1500.000.000.000.002.803.404.104.207.556.503.650.00
22Costa RicaPiñera9.138−83.3190.000.000.000.005.004.001.004.709.009.401.800.00
23Costa RicaPlayón9.533−84.3000.000.000.000.002.203.103.404.005.005.501.500.00
24Costa RicaQuepos9.461−84.1590.000.000.000.003.804.004.606.008.007.204.000.00
25Costa RicaRepunta9.300−83.6500.000.000.000.003.903.502.505.007.407.402.600.00
26Costa RicaSan Miguel10.417−83.9830.000.000.000.107.307.305.407.2010.5811.002.700.00
27Costa RicaSan_José9.633−84.2833.001.600.300.905.308.0010.508.905.906.807.204.80
28Costa RicaSanta Cruz Balanya10.261−85.5840.000.000.000.000.101.300.101.004.402.700.000.00
29Costa RicaVolcán Buenos Aires9.217−83.4500.000.000.000.706.204.652.005.9010.2511.203.700.00
30El SalvadorIlopango13.438−88.1580.000.000.000.000.000.500.000.402.700.600.000.00
31GuatemalaAsunción Mita14.334−89.7060.000.000.000.000.002.000.200.502.200.000.000.00
32GuatemalaCamotan15.044−91.0240.000.000.000.000.005.203.803.606.100.300.000.00
33GuatemalaChinique15.467−90.4080.200.000.000.000.004.803.803.704.902.900.800.20
34GuatemalaCoban15.109−90.6140.000.000.000.000.003.301.221.603.200.100.000.00
35GuatemalaEl asintal17.183−89.0000.000.000.000.000.000.301.100.500.500.700.300.10
36GuatemalaEsquipulas14.533−89.0330.000.000.000.000.003.301.101.603.700.200.000.00
37GuatemalaFlores17.250−88.7000.000.000.000.000.001.502.201.501.601.000.100.00
38GuatemalaHuehuetenango15.317−91.5030.000.000.000.000.001.900.100.202.150.000.000.00
39GuatemalaIsivumeth14.583−90.5330.000.000.000.000.002.650.700.702.700.000.000.00
40GuatemalaLa Ceibita14.493−89.8760.000.000.000.000.000.740.000.001.000.000.000.00
41GuatemalaLa Fragua14.964−89.5840.000.000.000.000.000.400.000.000.500.000.000.00
42GuatemalaLa Unión15.733−88.5830.400.000.000.000.001.305.405.002.402.001.251.00
43GuatemalaLabor Ovalle14.870−91.4800.000.000.000.000.002.000.600.702.300.000.000.00
44GuatemalaNebaj16.983−88.3170.000.000.000.000.000.601.501.302.001.000.300.00
45GuatemalaQuezada14.535−91.6780.000.000.000.002.527.174.005.2010.794.070.000.00
46GuatemalaSabana Grande14.030−91.0500.000.000.000.002.878.127.508.4012.007.500.000.00
47GuatemalaSacapulas14.301−90.7860.000.000.000.000.000.800.200.401.000.000.000.00
48GuatemalaSan Jerónimo16.517−89.8670.000.000.000.000.001.041.401.802.600.600.000.00
49GuatemalaSan Pedro Soloma14.632−91.2310.000.000.000.000.103.850.700.803.100.000.000.00
50GuatemalaSanta Cruz Balanya14.801−90.6400.000.000.000.000.001.100.601.002.100.000.000.00
51Honduras7871914.059−87.2160.000.000.000.000.000.400.100.101.000.200.000.00
52HondurasCholuteca13.318−87.1540.000.000.000.000.102.200.000.504.951.000.000.00
53HondurasJaral14.938−88.0050.000.000.000.000.005.038.768.566.853.441.320.20
54HondurasLa Ceiba15.730−86.8700.000.000.000.000.000.000.400.680.400.801.300.00
55HondurasLa Mesa15.400−87.9000.000.000.000.000.000.000.200.200.290.100.000.00
56HondurasPitosolo14.780−88.0100.000.000.000.000.002.504.505.604.233.000.600.00
57HondurasSjeronimo15.070−90.2500.000.000.000.000.801.201.202.000.000.000.000.00
58HondurasVallecillo14.520−87.4000.000.000.000.000.002.102.103.004.202.130.000.00
59Nicaragua4501813.339−86.3850.000.000.000.000.000.600.500.400.800.400.000.00
60Nicaragua5502612.763−85.6270.000.000.000.000.003.253.602.902.301.600.400.20
61Nicaragua6000612.427−86.9130.000.000.000.000.000.200.000.003.401.670.000.00
62Nicaragua6100912.583−81.7000.320.020.020.020.020.861.401.501.722.822.200.90
63Nicaragua6101013.350−81.3500.300.000.000.000.001.001.201.501.352.562.000.80
64Nicaragua6102512.633−87.1330.000.000.000.000.001.600.001.305.602.700.000.00
65Nicaragua6902612.143−86.1640.000.000.000.000.000.600.500.601.300.600.000.00
66Nicaragua6903111.722−86.0470.000.000.000.000.000.900.400.802.001.800.000.00
67Nicaragua6906811.435−85.8330.000.000.000.000.001.100.700.902.201.700.000.00
68NicaraguaCorinto12.517−87.2000.000.000.000.000.000.600.000.505.381.840.000.00
69NicaraguaJuigalpa12.100−85.3670.000.000.000.000.001.001.101.002.101.100.000.00
70NicaraguaOcotal13.625−86.4770.000.000.000.000.000.300.200.200.600.200.000.00
71NicaraguaPuerto Cabezas14.047−83.3751.100.200.000.000.204.205.604.903.004.002.711.80
72PanamáAgua_Clara9.364−79.7060.000.000.000.002.505.102.505.102.505.107.601.00
73PanamáAnton7.400−80.4500.000.000.000.000.001.000.901.001.203.001.900.00
74PanamáBalboa Heights8.959−79.5540.000.000.000.000.301.310.802.502.502.502.500.00
75PanamáBoca de Toabre9.424−79.5780.000.000.000.002.502.502.502.502.505.105.102.50
76PanamáBocas Toro9.340−82.2451.901.140.501.301.702.104.152.700.901.302.703.70
77PanamáCerro Punta8.508−81.0730.000.000.000.000.701.700.802.004.204.401.800.20
78PanamáCocle del Norte8.383−80.2670.000.000.000.000.000.900.500.800.702.101.100.00
79PanamáCuesta de Piedra8.065−78.3660.000.000.000.000.000.500.000.500.401.000.000.00
80PanamáDavid8.397−82.4280.000.000.000.001.201.851.102.504.004.201.500.00
81PanamáDivisa8.314−81.2090.000.000.000.001.203.000.602.807.057.001.950.00
82PanamáEscandalosa9.167−79.0830.000.000.000.001.002.001.502.002.504.002.500.00
83PanamáGatun9.268−79.9210.000.000.000.002.502.502.502.502.503.005.100.50
84PanamáIcacal9.205−80.1460.400.100.000.303.404.505.505.303.504.107.902.30
85PanamáSantiago8.140−80.7040.000.000.000.000.000.500.000.501.002.301.000.00
86PanamáTocumen9.066−79.3920.000.000.000.000.801.200.600.801.302.901.500.00
87PanamáTonosi8.087−80.9780.000.000.000.001.502.201.102.503.604.602.500.00
88PanamáValle Rico7.941−80.4180.000.000.000.000.000.200.000.000.300.700.000.00
Table
Table 2. Station metadata and 90th percentiles [mm/day] by month.
Table 2. Station metadata and 90th percentiles [mm/day] by month.
No.CountryStation Name/CodeLatitudeLongitudeJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
1BelicePswgia17.500−88.30014.287.602.782.3510.3421.2519.0017.8026.3525.1421.6016.00
2Costa RicaBagaces10.533−85.2500.000.000.000.7524.3424.6513.0020.8832.1528.988.400.40
3Costa RicaBatan10.083−83.33327.8022.4814.7420.7528.5825.2034.9829.4415.4022.8437.9038.47
4Costa RicaCatie9.883−83.63315.0014.057.9413.5024.6027.4025.7425.3025.2522.7426.4525.84
5Costa RicaCiudad_Quesada10.333−84.43327.6216.427.4011.2533.8840.2846.3242.7042.5043.7640.8544.08
6Costa RicaCoto 478.602−82.9689.806.4711.4029.2038.9443.3043.0149.9448.4552.7444.3018.26
7Costa RicaEl Carmen10.200−83.48031.4424.4118.0022.9032.5431.3541.0033.1021.0028.1446.2548.14
8Costa RicaFabio Baudrit10.000−84.2500.000.000.206.1028.2425.9019.2425.7830.7027.7415.202.10
9Costa RicaJilguero10.450−84.71738.1433.0023.0921.7537.8043.2044.9843.9436.6142.3643.8055.10
10Costa RicaJuan Sria10.000−84.2170.000.000.109.0027.7026.2018.3425.8431.6529.7816.611.80
11Costa RicaLa Argentina10.033−84.3500.000.000.003.9028.1827.4023.7029.0434.0031.8816.601.00
12Costa RicaLa Lola10.100−83.38029.2025.1416.0021.6034.5829.1537.3832.8419.4525.9840.7042.90
13Costa RicaLiberia10.599−85.5410.000.000.000.1024.7028.6017.0028.0837.5130.5411.100.20
14Costa RicaLimón9.950−83.01727.4425.0918.3828.3035.2434.1038.7032.8616.9019.8838.3039.54
15Costa RicaLinda Vista del Guarco
+Lankester CIGEFI		9.850−83.9002.502.101.062.6021.0020.5513.7016.7426.7525.5012.355.70
16Costa RicaLinda_Vista9.933−84.0830.100.000.102.8525.1226.7519.3425.4029.6128.0013.252.84
17Costa RicaLlorona9.400−84.0835.241.003.8018.3549.5040.6048.2048.1455.0059.4840.8015.20
18Costa RicaMonteverde10.136−84.19212.047.004.3014.6939.1740.6033.3638.4848.3546.0634.0520.96
19Costa RicaMonteverde+
Monteverde CIGEFI		10.133−84.8257.906.273.704.8530.1833.5024.5029.0441.2041.3021.7014.60
20Costa RicaNicoya10.150−85.4500.000.000.004.9029.4030.5025.4835.0437.9037.7614.850.00
21Costa RicaPalo_Seco9.433−84.1504.400.003.0419.8048.3043.6548.4250.1260.9057.8842.4517.92
22Costa RicaPiñera9.138−83.3191.501.609.8825.5543.0040.3537.6445.6447.0050.0029.107.68
23Costa RicaPlayón9.533−84.3000.680.001.7912.7538.1031.7034.1240.4843.0050.6225.359.88
24Costa RicaQuepos9.461−84.1594.021.203.8118.1542.6039.3042.0446.6451.6052.3535.9814.14
25Costa RicaRepunta9.300−83.6501.200.273.0616.1029.5027.4527.6033.5032.5534.5026.558.10
26Costa RicaSan Miguel10.417−83.98327.8223.5216.7622.4535.4442.7545.4441.1535.5036.7641.5544.72
27Costa RicaSan_José9.633−84.2831.301.095.3321.6646.5043.7040.9041.9754.0055.6233.609.34
28Costa RicaSanta Cruz Balanya10.261−85.5840.000.000.002.3023.8428.0219.2427.6435.4033.2410.550.00
29Costa RicaVolcán Buenos Aires9.217−83.4504.042.878.5427.9541.2041.8536.0443.6447.6550.3832.259.95
30El SalvadorIlopango13.438−88.1580.000.000.000.2021.8025.2022.5027.3229.7522.382.250.00
31GuatemalaAsunción Mita14.334−89.7060.000.000.000.6017.5830.9522.5023.4228.3016.580.000.00
32GuatemalaCamotan15.044−91.0240.000.000.002.8317.5427.5023.7024.0526.7017.643.601.00
33GuatemalaChinique15.467−90.4089.518.847.148.7016.2426.6521.0021.7027.5525.1618.4510.10
34GuatemalaCoban15.109−90.6140.100.000.002.6016.5823.5515.7018.8021.7713.613.100.30
35GuatemalaEl asintal17.183−89.00012.487.602.742.006.1019.2520.1817.0018.2621.9219.8014.46
36GuatemalaEsquipulas14.533−89.0330.900.500.142.6018.7030.7528.5031.2433.4520.522.351.40
37GuatemalaFlores17.250−88.70014.207.174.603.259.9627.9027.6423.9027.4025.2724.0717.58
38GuatemalaHuehuetenango15.317−91.5030.000.000.004.7013.0020.2511.4013.2223.0511.641.900.00
39GuatemalaIsivumeth14.583−90.5330.000.000.001.0012.4424.1518.9018.6424.6012.201.100.00
40GuatemalaLa Ceibita14.493−89.8760.000.000.000.0011.7423.4016.6418.1220.4011.000.200.00
41GuatemalaLa fragua14.964−89.5840.000.000.000.008.8418.3012.6013.0415.336.000.500.00
42GuatemalaLa Unión15.733−88.58324.1620.2011.8014.0518.7627.0036.0037.3432.6035.9639.2229.68
43GuatemalaLabor Ovalle14.870−91.4800.000.000.002.8514.3416.0510.8412.6415.4510.241.150.00
44GuatemalaNebaj16.983−88.31716.246.883.903.0011.4426.5025.1222.6031.0530.5824.3519.10
45GuatemalaQuezada14.535−91.6780.000.000.2013.0040.4849.5939.0041.0050.1039.1410.800.00
46GuatemalaSabana Grande14.030−91.0500.000.002.5420.0046.5855.9046.1949.6856.0051.3416.850.14
47GuatemalaSacapulas14.301−90.7860.000.000.000.4020.3931.6524.4829.1829.7722.001.850.00
48GuatemalaSan Jerónimo16.517−89.8677.243.201.442.5111.9423.4320.8320.8825.1522.4015.009.68
49GuatemalaSan Pedro Soloma14.632−91.2310.000.000.005.6515.8823.8013.2116.6423.0013.342.000.00
50GuatemalaSanta Cruz Balanya14.801−90.6400.000.000.000.3718.6031.4024.4225.8232.0721.971.350.00
51Honduras7871914.059−87.2160.300.100.101.3014.7416.858.089.8016.9012.082.600.40
52HondurasCholuteca13.318−87.1540.000.000.001.6032.1429.1018.6825.1437.6031.422.350.00
53HondurasJaral14.938−88.00513.819.344.346.8520.7438.9044.3442.7647.1337.1429.1716.72
54HondurasLa Ceiba15.730−86.87034.0620.3914.305.658.3023.9527.4424.5228.4651.0759.6540.38
55HondurasLa Mesa15.400−87.9008.054.501.040.655.7416.3114.8613.5016.0015.7018.1510.09
56HondurasPitosolo14.780−88.01016.3912.116.288.1518.0433.6339.2236.6640.9038.8028.5717.68
57HondurasSjeronimo15.070−90.2500.700.460.508.4017.0016.9621.2921.1414.836.912.500.50
58HondurasVallecillo14.520−87.4005.302.371.486.8523.2129.3526.4027.2233.2531.0813.925.90
59Nicaragua4501813.339−86.3850.650.300.100.0013.3814.157.409.8014.7015.703.201.30
60Nicaragua5502612.763−85.6273.802.501.201.3013.6825.0521.2620.8018.7517.789.205.60
61Nicaragua6000612.427−86.9130.000.000.000.0024.4424.1013.1621.0238.1029.688.250.00
62Nicaragua6100912.583−81.7007.313.791.721.8211.7123.3419.2521.5125.8129.6028.2112.91
63Nicaragua6101013.350−81.3508.725.172.202.1011.2419.6117.0520.1326.1632.9825.8011.34
64Nicaragua6102512.633−87.1330.000.000.000.0027.2832.7019.4427.3840.4032.686.700.00
65Nicaragua6902612.143−86.1640.000.000.000.0015.7016.6514.0416.0022.2020.304.000.20
66Nicaragua6903111.722−86.0470.100.000.000.0025.1623.9514.7416.0429.2031.127.050.80
67Nicaragua6906811.435−85.8330.340.000.000.0018.6923.8013.9220.3028.9025.829.702.00
68NicaraguaCorinto12.517−87.2000.000.000.000.0028.2428.0519.9625.2039.4930.645.530.00
69NicaraguaJuigalpa12.100−85.3670.800.300.000.0015.9819.6012.7115.3425.6519.205.401.30
70NicaraguaOcotal13.625−86.4770.100.000.000.2015.3415.207.409.1416.0512.182.100.30
71NicaraguaPuerto Cabezas14.047−83.37513.348.474.505.1520.0436.5341.8635.0031.9035.0831.5017.20
72PanamáAgua_Clara9.364−79.70610.205.105.1017.8035.6038.1033.0040.6038.1045.7053.3033.00
73PanamáAnton7.400−80.4500.000.000.002.1718.9620.0518.2421.6422.4528.4025.908.34
74PanamáBalboa Heights8.959−79.5542.500.000.007.6023.1922.9021.7521.9520.9927.9027.9015.20
75PanamáBoca de Toabre9.424−79.57810.207.605.6720.3033.0033.0033.0030.5027.9030.5043.2030.50
76PanamáBocas Toro9.340−82.24524.4520.3016.9426.5531.0228.8534.0229.5420.4021.2432.2538.00
77PanamáCerro Punta8.508−81.0733.341.901.446.0024.0428.4022.5828.1433.0533.6424.7510.44
78PanamáCocle del Norte8.383−80.2670.000.000.001.9516.6819.7519.7019.7820.4525.9424.808.84
79PanamáCuesta de Piedra8.065−78.3660.400.000.004.3917.6816.7516.2717.3417.8626.1722.3810.61
80PanamáDavid8.397−82.4280.600.001.009.1033.6033.6031.2434.9437.7536.1427.957.50
81PanamáDivisa8.314−81.2090.000.000.007.8037.9437.0032.8643.5446.8446.4433.458.10
82PanamáEscandalosa9.167−79.0831.000.500.505.5027.2026.5021.2025.5031.5038.5031.5011.00
83PanamáGatun9.268−79.9217.605.102.5016.3530.5027.9027.9030.7528.2133.0040.6025.40
84PanamáIcacal9.205−80.14610.665.645.6419.6042.2836.8042.3436.9035.8541.8453.9537.28
85PanamáSantiago8.140−80.7040.000.000.000.8023.5423.7517.0821.8827.4028.8224.004.74
86PanamáTocumen9.066−79.3920.300.000.005.7524.3425.0019.1422.9426.0031.3027.1011.64
87PanamáTonosi8.087−80.9780.200.000.0011.3034.3032.1525.1435.0035.4237.8429.908.98
88PanamáValle Rico7.941−80.4180.000.000.000.0010.8813.858.5411.6817.0523.6813.952.40
Table
Table 3. Classification of tropical cyclones according to the Saffir–Simpson scale (for hurricanes) and other related classifications. Wind speed refers to the 1-min maximum sustained winds.
Table 3. Classification of tropical cyclones according to the Saffir–Simpson scale (for hurricanes) and other related classifications. Wind speed refers to the 1-min maximum sustained winds.
CategoryWindspeed [km/h]Damage
Five (major)>252Catastrophic
Four (major)209–251Catastrophic
Three (major)178–208Devastating
Two154–177Extremely dangerous
One119–153Very dangerous
Tropical storm63–118-
Tropical depression<62-
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Hidalgo, H.G.; Alfaro, E.J.; HernÃandez-Castro, F.; Pérez-Briceño, P.M. Identification of Tropical Cyclones’ Critical Positions Associated with Extreme Precipitation Events in Central America. Atmosphere 2020, 11, 1123. https://doi.org/10.3390/atmos11101123

AMA Style

Hidalgo HG, Alfaro EJ, HernÃandez-Castro F, Pérez-Briceño PM. Identification of Tropical Cyclones’ Critical Positions Associated with Extreme Precipitation Events in Central America. Atmosphere. 2020; 11(10):1123. https://doi.org/10.3390/atmos11101123

Chicago/Turabian Style

Hidalgo, Hugo G., Eric J. Alfaro, Franklin HernÃandez-Castro, and Paula M. Pérez-Briceño. 2020. "Identification of Tropical Cyclones’ Critical Positions Associated with Extreme Precipitation Events in Central America" Atmosphere 11, no. 10: 1123. https://doi.org/10.3390/atmos11101123

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop