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Precipitation: Forecasting and Climate Projections
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Precipitation: Forecasting and Climate Projections

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 36186

Special Issue Editors

Dr. Konstantia Tolika

E-Mail Website
Guest Editor
Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece
Interests: climate variability; artificial neural networks; atmosphere; regional climate modeling; extreme events
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis Pytharoulis

E-Mail Website
Guest Editor
Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece
Interests: synoptic and dynamic meteorology; numerical weather prediction; operational weather forecasting; land/sea–air interaction; extreme weather events; pyro-meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precipitation is one of the main meteorological and climatological parameters being affected and affecting several aspects of the complex Atmosphere-Earth-Ocean system. The thorough and accurate study of the mechanisms and factors that interact and determine precipitation characteristics (regime, extremes, trends, etc.), as well as its prediction/future simulations on a global and/or regional scale is found to be crucial for humans, ecosystems, environment and life in our planet.

The overarching goal of this Special Issue is to comprise review an original observational, theoretical and modelling studies on forecasting of precipitation at all spatio-temporal scales (from short-term up to seasonal predictions) and climate projections both on a global and regional scale.

Topics of interest may include, but are not limited to, the following:

  • Numerical weather prediction focusing on precipitation, including operational forecasting and evaluation
  • Physical Parameterizations affecting precipitation
  • Sensitivity experiments
  • Climate models (global and regional scale)
  • Present day and future projections on precipitation (mean and extremes)
  • Climate model evaluations

Dr. Konstantia (Dia) Tolika
Dr. Ioannis Pytharoulis
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (9 papers)

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Research

31 pages, 9898 KiB  
Article
Sensitivity of Simulations of Zambian Heavy Rainfall Events to the Atmospheric Boundary Layer Schemes
by Mary-Jane M. Bopape, David Waitolo, Robert S. Plant, Elelwani Phaduli, Edson Nkonde, Henry Simfukwe, Stein Mkandawire, Edward Rakate and Robert Maisha
Climate 2021, 9(2), 38; https://doi.org/10.3390/cli9020038 - 23 Feb 2021
Cited by 1 | Viewed by 3006
Abstract
Weather forecasting relies on the use of numerical weather prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is [...] Read more.
Weather forecasting relies on the use of numerical weather prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is represented in planetary boundary layer (PBL) schemes. In this study, we evaluate the sensitivity of heavy rainfall events over Zambia to four different PBL schemes in the Weather Research and Forecasting (WRF) model using a parent domain with a 9 km grid length and a 3 km grid spacing child domain. The four PBL schemes are the Yonsei University (YSU), nonlocal first-order medium-range forecasting (MRF), University of Washington (UW) and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes. Simulations were done for three case studies of extreme rainfall on 17 December 2016, 21 January 2017 and 17 April 2019. The use of YSU produced the highest rainfall peaks across all three cases; however, it produced performance statistics similar to UW that are higher than those of the two other schemes. These statistics are not maintained when adjusted for random hits, indicating that the extra events are mainly random rather than being skillfully placed. UW simulated the lowest PBL height, while MRF produced the highest PBL height, but this was not matched by the temperature simulation. The YSU and MYNN PBL heights were intermediate at the time of the peak; however, MYNN is associated with a slower decay and higher PBL heights at night. WRF underestimated the maximum temperature during all cases and for all PBL schemes, with a larger bias in the MYNN scheme. We support further use of the YSU scheme, which is the scheme selected for the tropical suite in WRF. The different simulations were in some respects more similar to one another than to the available observations. Satellite rainfall estimates and the ERA5 reanalysis showed different rainfall distributions, which indicates a need for more ground observations to assist with studies like this one. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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20 pages, 6271 KiB  
Article
Forecasting Intense Cut-Off Lows in South Africa Using the 4.4 km Unified Model
by Tshimbiluni Percy Muofhe, Hector Chikoore, Mary-Jane Morongwa Bopape, Nthaduleni Samuel Nethengwe, Thando Ndarana and Gift Tshifhiwa Rambuwani
Climate 2020, 8(11), 129; https://doi.org/10.3390/cli8110129 - 07 Nov 2020
Cited by 8 | Viewed by 4406
Abstract
Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They occur during all the above and often result in floods and snowfalls during the winter months, disrupting economic activities and causing extensive [...] Read more.
Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They occur during all the above and often result in floods and snowfalls during the winter months, disrupting economic activities and causing extensive damage to infrastructure. This paper examines the evolution and circulation patterns associated with cases of severe COLs over South Africa. We evaluate the performance of the 4.4 km Unified Model (UM) which is currently used operationally by the South African Weather Service (SAWS) to simulate daily rainfall. Circulation variables and precipitation simulated by the UM were compared against European Centre for Medium-Range Weather Forecast’s (ECMWF’s) ERA Interim re-analyses and GPM precipitation at 24-hour timesteps. We present five recent severe COLs, which occurred between 2016 and 2019, that had high impact and found a higher model skill when simulating heavy precipitation during the initial stages than the dissipating stages of the systems. A key finding was that the UM simulated the precipitation differently during the different stages of development and location of the systems. This is mainly due to inaccurate placing of COL centers. Understanding the performance and limitations of the UM model in simulating COL characteristics can benefit severe weather forecasting and contribute to disaster risk reduction in South Africa. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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19 pages, 4339 KiB  
Article
Convection Parametrization and Multi-Nesting Dependence of a Heavy Rainfall Event over Namibia with Weather Research and Forecasting (WRF) Model
by Sieglinde Somses, Mary-Jane M. Bopape, Thando Ndarana, Ann Fridlind, Toshihisa Matsui, Elelwani Phaduli, Anton Limbo, Shaka Maikhudumu, Robert Maisha and Edward Rakate
Climate 2020, 8(10), 112; https://doi.org/10.3390/cli8100112 - 07 Oct 2020
Cited by 10 | Viewed by 3843
Abstract
Namibia is considered to be one of the countries that are most vulnerable to climate change due to its generally dry climate and the percentage of its population that rely on subsistence agriculture for their livelihoods. Early-warning systems are an important aspect of [...] Read more.
Namibia is considered to be one of the countries that are most vulnerable to climate change due to its generally dry climate and the percentage of its population that rely on subsistence agriculture for their livelihoods. Early-warning systems are an important aspect of adapting to climate change. Weather forecasting relies on the use of numerical weather prediction models and these need to be configured properly. In this study, we investigate the effects of using multi-nests and a convection scheme on the simulation of a heavy rainfall event over the north-western region of Kunene, Namibia. The event, which was associated with a cut-off low system, was short-lived and resulted in over 45 mm of rainfall in one hour. For the multi-nest, a 9 km grid-length parent domain is nested within the Global Forecast System (GFS) simulations, which in turn forces a 3 km grid spacing child domain. A different set of simulations are produced using a single nest of 3 km grid spacing, nested directly inside the GFS data. The simulations are produced with the convection scheme switched on and off. The impact of a single versus multi-nest is found to be small in general, with slight differences in the location of high rainfall intensity. Switching off the convection schemes results in high rainfall intensity and increased detail in the simulations, including when a grid spacing of 9 km is used. Using a grid spacing of 3 km with the convection scheme on, results in a loss of detail in the simulations as well as lower rainfall amounts. The study shows a need for different configurations to be tested before an optimum configuration can be selected for operational forecasting. We recommend further tests with different synoptic forcing and convection schemes to be conducted to identify a suitable configuration for Namibia. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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23 pages, 15657 KiB  
Article
Investigating the Role of Extreme Synoptic Patterns and Complex Topography During Two Heavy Rainfall Events in Crete in February 2019
by Konstantinos Lagouvardos, Stavros Dafis, Christos Giannaros, Athanassios Karagiannidis and Vassiliki Kotroni
Climate 2020, 8(7), 87; https://doi.org/10.3390/cli8070087 - 16 Jul 2020
Cited by 8 | Viewed by 4703
Abstract
During February 2019, two severe storms affected the island of Crete, located in south Greece. Both storms produced excessive rainfall, provoking severe damages, especially in the western part of Crete. The role of the prevailing synoptic patterns and the interaction of the flow [...] Read more.
During February 2019, two severe storms affected the island of Crete, located in south Greece. Both storms produced excessive rainfall, provoking severe damages, especially in the western part of Crete. The role of the prevailing synoptic patterns and the interaction of the flow with the high mountains of Crete were investigated. For this purpose, a variety of observational and numerical model data were exploited, including data from a dense rain gauge network, satellite imagery, and model analysis of various parameters describing the stability of the impinging flow. The first storm was a long-lasting event, with convective outbreaks embedded in a more stratiform rainfall pattern. The second storm was brief but mostly convection dominated. The analysis of the available data underlined the role of the low-level convergence upstream of the mountains during both storms, highlighting similarities and differences, as well as the role of the stability of the impinging flow. High soil moisture content was also evidenced as a key ingredient for the severe flooding that occurred during the second storm. This work complements similar studies on the role of Mediterranean islands and their topography on the spatial and temporal distribution of extreme rainfall. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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21 pages, 9353 KiB  
Article
Bias Correction of RCM Precipitation by TIN-Copula Method: A Case Study for Historical and Future Simulations in Cyprus
by Georgia Lazoglou, George Zittis, Christina Anagnostopoulou, Panos Hadjinicolaou and Jos Lelieveld
Climate 2020, 8(7), 85; https://doi.org/10.3390/cli8070085 - 04 Jul 2020
Cited by 7 | Viewed by 3439
Abstract
Numerical models are being used for the simulation of recent climate conditions as well as future projections. Due to the complexity of the Earth’s climate system and processes occurring at sub-grid scales, model results often diverge from the observed values. Different methods have [...] Read more.
Numerical models are being used for the simulation of recent climate conditions as well as future projections. Due to the complexity of the Earth’s climate system and processes occurring at sub-grid scales, model results often diverge from the observed values. Different methods have been developed to minimize such biases. In the present study, the recently introduced “triangular irregular networks (TIN)-Copula” method was used for the bias correction of modelled monthly total and extreme precipitation in Cyprus. The method was applied to a 15-year historical period and two future periods of the same duration. Precipitation time-series were derived from a 12-km resolution EURO-CORDEX regional climate simulation. The results show that the TIN-Copula method significantly reduces the positive biases between the model results and observations during the historical period of 1986–2000, for both total and extreme precipitation (>80%). However, the level of improvement differs temporally and spatially. For future periods, the model tends to project significantly higher total precipitation rates prior to bias correction, while for extremes the differences are smaller. The adjustments slightly affect the overall climate change signal, which tends to be enhanced after bias correction, especially for total precipitation and for the autumn period. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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23 pages, 3635 KiB  
Article
Analysis of the Spatio-Temporal Variability of Precipitation and Drought Intensity in an Arid Catchment in South Africa
by Achamyeleh G. Mengistu, Weldemichael A. Tesfuhuney, Yali E. Woyessa and Leon D. van Rensburg
Climate 2020, 8(6), 70; https://doi.org/10.3390/cli8060070 - 01 Jun 2020
Cited by 8 | Viewed by 4090
Abstract
Water deficit is high and precipitation varies spatio-temporally in arid areas. This study was conducted to analyse the spatio-temporal variability of precipitation and drought intensity in an arid catchment in South Africa. The Soil and Water Assessment Tool (SWAT) was used to estimate [...] Read more.
Water deficit is high and precipitation varies spatio-temporally in arid areas. This study was conducted to analyse the spatio-temporal variability of precipitation and drought intensity in an arid catchment in South Africa. The Soil and Water Assessment Tool (SWAT) was used to estimate the spatio-temporal precipitation where nine meteorological stations were used as input to the model. The model was calibrated and validated by regionalization with a physical similarity approach. SWAT only predicts precipitation at sub-basin level. Hence, the mean precipitation was further interpolated by using the inverse distance weighted method (IDW). The Mann–Kendall trend test shows that there was no trend in annual precipitation whereas in the monthly precipitation there was a 0.01 mm decrease. Daily precipitation varied from 0.1 to 4 mm whereas in a monthly basis, it varied from 6 mm (September) to 43.4 mm (February). The annual precipitation varied from 169 mm (1983) to 415 mm (2003) with a long-term mean of 280.8 mm. Precipitation is also highly variable in space throughout the catchment. Generally, annual precipitation decreased from north to south; however, during the winter season, the reverse was true due to the influence of rain-bearing condition from the south- western direction. Based on the aridity index (AI), the catchment is categorized as arid. The SPI shows that the 1983 drought was the worst whereas the 2003 and 2004 years were relatively wet. The results from this study provide baseline information for further research in climate change adaptation and environmental monitoring programs in the region. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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20 pages, 5077 KiB  
Article
Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices
by Heng Qian and Shi-Bin Xu
Climate 2020, 8(4), 53; https://doi.org/10.3390/cli8040053 - 09 Apr 2020
Cited by 4 | Viewed by 2544
Abstract
Autumn precipitation (AP) has important impacts on agricultural production, water conservation, and water transportation in the middle and lower reaches of the Yangtze River Basin (MLYRB; 25°–35° N and 105°–122° E). We obtain the main empirical orthogonal function (EOF) modes of the interannual [...] Read more.
Autumn precipitation (AP) has important impacts on agricultural production, water conservation, and water transportation in the middle and lower reaches of the Yangtze River Basin (MLYRB; 25°–35° N and 105°–122° E). We obtain the main empirical orthogonal function (EOF) modes of the interannual variation in AP based on daily precipitation data from 97 stations throughout the MLYRB during 1980–2015. The results show that the first leading EOF mode accounts for 30.83% of the total variation. The spatial pattern shows uniform change over the whole region. The variance contribution of the second mode is 16.13%, and its spatial distribution function shows a north-south phase inversion. Based on previous research and the physical considerations discussed herein, we include 13 climate indices to reveal the major predictors. To obtain an acceptable prediction performance, we comprehensively rank the climate indices, which are sorted according to the values of the new standardized algorithm of information flow (NIF, a causality-based approach) and correlation coefficient (a traditional climate diagnostic tool). Finally, Tropical Indian Ocean Dipole (TIOD), Arctic Oscillation (AO), and other four indicators are chosen as the final predictors affecting the first mode of AP over the MLYRB; NINO3.4 SSTA (NINO3.4), Atlantic-European Circulation E Pattern (AECE), and other four indicators are the major predictors for the second mode. In the final prediction experiment, considering the time series prediction of principal components (PCs) to be a small-sample problem, the Bayesian linear regression (BLR) model is used for the prediction. The experimental results reveal that the BLR model can effectively capture the time series trends of the first two modes (the correlation coefficients are greater than 0.5), and the overall performance is significantly better than that of the multiple linear regression (MLR) model. The prediction factors and precipitation prediction results identified in this study can be referenced to rapidly obtain climatological information for AP over the MLYRB and improve the regional prediction of AP elsewhere, which will also help policymakers prepare appropriate adaptation and mitigation measures for future climate change. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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23 pages, 4675 KiB  
Article
Effects of Climate Change on Precipitation and the Maximum Daily Temperature (Tmax) at Two US Military Bases with Different Present-Day Climates
by Jovan M. Tadić and Sébastien C. Biraud
Climate 2020, 8(2), 18; https://doi.org/10.3390/cli8020018 - 22 Jan 2020
Cited by 3 | Viewed by 3378
Abstract
In this study, the effects of climate change on precipitation and the maximum daily temperature (Tmax) at two USA locations that have different climates—the Travis Airforce Base (AFB) in California [38.27° N, 121.93° W] and Fort Bragg (FBR) in North Carolina [...] Read more.
In this study, the effects of climate change on precipitation and the maximum daily temperature (Tmax) at two USA locations that have different climates—the Travis Airforce Base (AFB) in California [38.27° N, 121.93° W] and Fort Bragg (FBR) in North Carolina [35.14 N, 79.00 W]—are analyzed. The effects of climate change on central tendency, tail distributions, and both auto- and cross-covariance structures in precipitation and Tmax fields for three time periods in the 21st century centered on the years 2020, 2050, and 2100 were analyzed. It was found that, on average, Tmax under the Representative Concentration Pathway (RCP) 4.5 emission scenario is projected to increase for the years 2020, 2050, and 2100 by 1.1, 2.0, and 2.2 °C, respectively, for AFB, and 0.9, 1.2, and 1.6 °C, respectively, for FBR, while under the RCP8.5 emission scenario Tmax will increase by 1.1, 1.9, and 2.7 °C, respectively, for AFB, and 0.1, 1.5, and 2.2 °C, respectively, for FBR. The climate change signal in precipitation is weak. The results show that, under different emission scenarios, events considered to be within 1% of the most extreme events in the past will become ~13–30 times more frequent for Tmax, ~and 0.05–3 times more frequent for precipitation in both locations. Several analytical methods were deployed in a sequence, creating an easily scalable framework for similar analyses in the future. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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19 pages, 5920 KiB  
Article
Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint
by Alfonso Gutierrez-Lopez, Ivonne Cruz-Paz and Martin Muñoz Mandujano
Climate 2019, 7(11), 131; https://doi.org/10.3390/cli7110131 - 12 Nov 2019
Cited by 4 | Viewed by 5764
Abstract
Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most [...] Read more.
Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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