Search Results (13)

This study presents an objective scheme to construct hurricane bogus vortices based on satellite microwave scatterometer observations of sea surface wind vectors. When specifying a bogus vortex using Fujita’s formula, the required parameters include the center position and the radius of the maximum gradient of sea level pressure (R₀). We first propose determining the tropical cyclone (TC) center position as the cyclonic circulation center obtained from sea surface wind observations and then establishing a regression model between R₀ and the radius of 34-kt sea surface wind of scatterometer observations. The radius of 34-kt sea surface wind (R₃₄) is commonly used as a measure of TC size. The center positions determined from HaiYang-2B/2C/2D Scatterometers, MetOp-B/C Advanced Scatterometers, and FengYun-3E Wind Radar compared favorably with the axisymmetric centers of hurricane rain/cloud bands revealed by Advanced Himawari Imager observations of brightness temperature for the western Pacific landfalling typhoons Doksuri, Khanun, and Haikui in 2023. Furthermore, regression equations between R₀ and the scatterometer-determined radius of 34-kt wind are developed for tropical storms and category-1, -2, -3, and higher hurricanes over the Northwest Pacific (2022–2023). The bogus vortices thus constructed are more realistic than those built without satellite sea surface wind observations. Full article

Interdecadal variations, since the middle of the 20th century, in the seasonal cycle of Southern Hemisphere extratropical synoptic scale weather systems, are studied and related to associated anomalies in Southern Australian rainfall over south-west Western Australia (SWWA) and southeast Australia (SEA). A data-driven method is employed in which atmospheric fluctuations, specified from 6-hourly lower-tropospheric reanalysis data, are spectrally analysed in space and time to determine the statistics of the intensity and growth rates of growing and decaying eddies. Extratropical storms, blocking and north-west cloud band weather types are investigated in two frequency bands, with periods less than 4 days and between 4 and 8 days, and in three growth rate and three decay rate bins. Southern Australian rainfall variability is found to be most related to changes in explosive storms particularly in autumn and winter. During the first 10 years of the Australian Millennium Drought (AMD), from 1997 to 2006, dramatic changes in rainfall and storminess occurred. Rainfall declines ensued over SEA in all seasons, associated with corresponding reductions in the intensity of fast-growing storms with periods less than 4 days. These changes, compared with the 20-year timespans of 1949 to 1968 and 1975 to 1994, also took place for the longer duration of 1997 to 2016, apart from summer. Over SWWA, autumn and winter rainfall totals have decreased systematically with time for each of the 10-year and 20-year timespans analysed. Southern Australian rainfall variability is also found to be closely related to the local, hemispheric or global features of the circulation of the atmosphere and oceans that we characterise by indices. Local circulation indices of sea level pressure and 700 hPa zonal winds are good predictors of SWWA and SEA annual rainfall variability particularly in autumn and winter with vertical velocity generally less so. The new Subtropical Atmospheric Jet (SAJ) and the Southern Ocean Regional Dipole (SORD) indices are found to be the most skilful non-local predictors of cool season SWWA rainfall variability on annual and decadal timescales. The Indian Ocean Dipole (IOD) and Southern Oscillation Index (SOI) are the strongest non-local predictors of SEA annual rainfall variability from autumn through to late spring, while on the decadal timescale, different indices dominate for different 3-month periods. Full article

Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, random forest (RF), support vector regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia’s geographical regions, RF consistently delivered the best performance. In the northwest (NW), the RF model achieved the best performance with the CO3 combination, having an R² of 0.63 and an RMSE of 125.39 m³/plot. In the southwest (SW), the RF model also performed exceptionally well, achieving an R² of 0.73 and an RMSE of 128.86 m³/plot with the CO4 variable combination. In the northeast (NE), the RF model outperformed other ML models, achieving an R² of 0.64 and an RMSE of 133.77 m³/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R² of 0.70 and an RMSE of 21,120.72 m³/plot. These results underscore RF’s precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy. Full article

The seasonal variability of Southern Hemisphere (SH) synoptic-scale weather systems is analysed for the 20-year timespan 1997 to 2016. The relationships between the SH jet streams and storm tracks based on lower tropospheric circulation anomalies filtered into the high-pass (periods < 4 days) and band-pass (periods between 4 and 8 days) types are examined based on 6-hourly reanalysis data. Leading Empirical Orthogonal Functions (EOFs) and storm tracks based on all (growing and decaying) disturbances are determined. As well, the structure and standard deviations of streamfunction fluctuations are determined separately in three growth rate and three decay rate bins focusing on explosive growth and decay. In all cases, and in each season, the band-pass storm tracks are more zonally symmetric than the high-pass standard deviations and this is also reflected by the EOFs. Leading EOFs in both bands are monopole wavetrains of highs and lows located in the storm tracks with some band-pass disturbances having dipole structures consistent with blocking and northwest cloud bands. EOFs based on the bin with slow-growing fluctuations are structurally similar to the standard EOFs based on all disturbances. EOFs for moderately and explosively growing disturbances are increasingly displaced equatorward with a larger growth rate. Full article

Multiple interactions of three typhoons and a mid-latitude cloud band-associated with a trough (MLCT) were investigated from 1 July to 10 July 2015, using the Korea Communication, Ocean, and Meteorological Satellite (COMS) satellite images and two kinds of meteorological models, such as UM-KMA (U.K.) and WRF-3.6 (U.S.A.), to generate the horizontal structure of wind and relative humidity, streamline, and moisture flux. As severe tropical storm (STS) Linfa moved toward the warmer area with a sea surface temperature (SST) of 31 °C in the northern South China Sea, it obtained not only more moisture by thermal convection of water vapor from the sea surface toward the lower atmosphere but also more momentum by its multiple interactions with both the MLCT and Typhoon (TY) Chan-Hom. Through their mutual interactions, mutual feedback of moisture and momentum fluxes could accelerate the formation of clouds in their systems and an asymmetric structure of their circulations. After Linfa weakened due to the increased friction of the shallower sea bottom close to the Chinese coast and its disconnection from the MLCT, later it became re-intensified with the increased wind speeds by a stronger interaction with more intensified TY Chan-Hom entering the path of the Kuroshio Current of SST 31 °C, which could supply additional moisture through thermal convection of water vapor into its system. Then, further interaction between the rapidly developed TY Nangka following behind and the MLCT enhanced the transfer of moisture and momentum fluxes from Chan-Hom into Linfa. Finally, after STS Linfa made landfall on the Chinese coast, it decayed into a weak low-pressure system before its dissipating, due to the weakening of its cyclonic circulation through the increased friction by the shallower sea bottom and the surrounding lands. Full article

We obtained high-resolution spectra of Jupiter between 4.6 and 5.4 µm using NIRSPEC on the Keck 2 telescope in February 2017. We measured the spatial variation of NH${}_3$, H${}_2$O, and the pressure level of deep (p > 3 bar) clouds using two geometries. We aligned the slit north–south on Jupiter’s Central Meridian to measure the spatial variation of the gas composition and cloud structure between 66${}^{°}$N and 70${}^{°}$S. With the slit aligned east–west, we also examined the longitudinal variation at two regions of the North Equatorial Belt (NEB) at 18${}^{°}$N and at 8${}^{°}$N near the latitude of the Galileo Probe entry site. We used the integrated line absorption, also known as the equivalent width, of deuterated methane (CH${}_3$D) at 4.66 µm to derive the pressure level of deep clouds between 3 and 7 bar. From thermochemical models, these are most likely water clouds. At the location of a deep cloud revealed by HST methane-band imaging, we found spectroscopic evidence for an opaque cloud at the 5 bar level. We also identified regions on Jupiter that lacked deep clouds but exhibited evidence for upper clouds and enhanced NH${}_3$. We estimated column-averaged mole fractions of H${}_2$O and NH${}_3$ above the opaque lower boundary of the deep cloud. The meridional scan exhibited significant belt-zone structure with retrieved NH${}_3$ abundances in the 200–400 ppm range above the opaque lower cloud, except for a depletion (down to 90 ppm) in the NEB. Water in Jupiter’s belts varies from a maximum of 7 ppm at 8${}^{°}$S to a minimum of 1.5 ppm at 23${}^{°}$S. We found evidence for water clouds and enhanced NH${}_3$ and H${}_2$O in the South Equatorial Belt Outbreak region at 13${}^{°}$S. The NEB is a heterogeneous region with significant variation in all of these quantities. The NH${}_3$ abundance at 18${}^{°}$N and 8${}^{°}$N varies with the longitude with mole fractions between 120 and 300 ppm. The H${}_2$O abundance at these same latitudes varies with the longitude with mole fractions between 3 and 10 ppm. Our volatile mole fractions apply to the 5 to 8 bar pressure range (or to the level of an opaque cloud top where found at shallower pressure); therefore, they imply a deeper gradient continuing to increase toward higher concentrations detected by the Galileo Probe Mass Spectrometer at 11 and 20 bar. Hot Spots in the NEB exhibit minimal cloud opacity; however, they lack prominent anomalies in the concentrations of NH${}_3$ or H${}_2$O. Full article

Landsat has provided the longest fine resolution data archive of Earth’s environment since 1972; however, one of the challenges in using Landsat data for various applications is its frequent large data gaps and heavy cloud contaminations. One pressing research topic is to generate the regular time series by integrating coarse-resolution satellite data through data fusion techniques. This study presents a novel spatiotemporal fusion (STF) method based on a depthwise separable convolutional neural network (DSC), namely, STFDSC, to generate Landsat-surface reflectance time series at 8-day intervals by fusing Landsat 30 m with high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data. The STFDSC method consists of three main stages: feature extraction, feature fusion and prediction. Features were first extracted from Landsat and MODIS surface reflectance changes, and the extracted multilevel features were then stacked and fused. Both low-level and middle-level features that were generally ignored in convolutional neural network (CNN)-based fusion models were included in STFDSC to avoid key information loss and thus ensure high prediction accuracy. The prediction stage generated a Landsat residual image and is combined with original Landsat data to obtain predictions of Landsat imagery at the target date. The performance of STFDSC was evaluated in the Greater Khingan Mountains (GKM) in Northeast China and the Ziwuling (ZWL) forest region in Northwest China. A comparison of STFDSC with four published fusion methods, including two classic fusion methods (FSDAF, ESTARFM) and two machine learning methods (EDCSTFN and STFNET), was also carried out. The results showed that STFDSC made stable and more accurate predictions of Landsat surface reflectance than other methods in both the GKM and ZWL regions. The root-mean-square-errors (RMSEs) of TM bands 2, 3, 4, and 7 were 0.0046, 0.0038, 0.0143, and 0.0055 in GKM, respectively, and 0.0246, 0.0176, 0.0280, and 0.0141 in ZWL, respectively; it can be potentially used for generating the global surface reflectance and other high-level land products. Full article

In this study, the three-dimensional (3D) warm-core structures of the Northwest Pacific typhoons Francisco, Lekima, and Krosa in August 2019 are retrieved from the Fengyun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) observations of brightness temperature. Due to the lack of two window channels at 23.8 GHz and 31.4 GHz, an empirical cloud detection algorithm based on 50.3 GHz bias-corrected observations-minus-backgrounds is applied to obtain clear-sky observations for the multiple linear regression retrieval algorithm. The MWTS-2 cloud-affected channels 3–5 are not used to retrieve temperatures under cloudy conditions to eliminate low-tropospheric cold anomalies. The multiple linear regression coefficients are obtained based on MWTS-2 brightness temperatures and the temperatures from the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) in the training period of three weeks before the month of targeted typhoons. The proposed MWTS-2 warm-core retrieval can well capture the radial and vertical temporal evolutions of the temperature anomalies of the typhoons Francisco, Lekima, and Krosa. The sizes of the warm-core anomalies of typhoons Lekima and Krosa retrieved by the MWTS-2 are horizontally and vertically similar to and stronger than those of the ERA5. Compared with the ERA5 reanalysis in August 2019, the biases for MWTS-2 temperature retrievals are smaller than ±0.25 K, with root-mean-square errors (RMSEs) smaller than and 2.0 K at all altitudes. Additionally, the location of the 250-hPa maximum temperature anomaly retrieved by the MWTS-2 is closer to the best track than that of the ERA5. A weak warm-core around 200 hPa and a cold-core anomaly in the middle troposphere are also found in the outer rain bands region due to the effect of evaporation of rainfall. Full article

The detection and removal of erroneous pixels is a critical pre-processing step in producing chlorophyll-a (chl-a) concentration values to adequately understand the bio-physical oceanic process using optical satellite data. Geostationary Ocean Color Imager (GOCI) chl-a images revealed that numerous speckle noises with enormously high and low values were randomly scattered throughout the seas around the Korean Peninsula as well as in the Northwest Pacific. Most of the previous methods used to remove abnormal chl-a concentrations have focused on inhomogeneity in spatial features, which still frequently produce problematic values. Herein, a scheme was developed to detect and eliminate chl-a speckles as well as erroneous pixels near the boundary of clouds; for the purpose, a deep neural network (DNN) algorithm was applied to a large-sized GOCI database from the 6-year period of 2012–2017. The input data of the proposed DNN model were composed of the GOCI level-2 remote-sensing reflectance of each band, chl-a concentration image, median filtered, and monthly climatology chl-a image. The quality of the individual images as well as the monthly composites of chl-a data was improved remarkably after the DNN speckle-removal procedure. The quantitative analyses showed that the DNN algorithm achieved high classification accuracy with regard to the detection of error pixels with both very high and very low chl-a values, and better performance compared to the general arithmetic algorithms of the median filter and threshold scheme. This implies that the implemented method can be useful for investigating not only the short-term variations based on hourly chl-a data but also long-term variabilities with composite products of the GOCI chl-a concentration over the span of a decade. Full article

We used the observations from Atmospheric Infrared Sounder (AIRS) onboard Aqua over the northwest Pacific Ocean from 2006–2015 to study the relationships between (i) tropical cyclone (TC) temperature structure and intensity and (ii) cloud macro-/micro-physical properties and TC intensity. TC intensity had a positive correlation with warm-core strength (correlation coefficient of 0.8556). The warm-core strength increased gradually from 1 K for tropical depression (TD) to >15 K for super typhoon (Super TY). The vertical areas affected by the warm core expanded as TC intensity increased. The positive correlation between TC intensity and warm-core height was slightly weaker. The warm-core heights for TD, tropical storm (TS), and severe tropical storm (STS) were concentrated between 300 and 500 hPa, while those for typhoon (TY), severe typhoon (STY), and Super TY varied from 200 to 350 hPa. Analyses of the cloud macro-/micro-physical properties showed that the top of TC cloud systems mainly consisted of ice clouds. For TCs of all intensities, areas near the TC center showed lower cloud-top pressures and lower cloud-top temperatures, more cloud fractions, and larger ice-cloud effective diameters. With the increase in TC intensity, the levels of ice clouds around the TC center became higher and the spiral cloud-rain bands became larger. When a TC developed into a TY, STY, or Super TY, the convection in the clouds was stronger, releasing more heat, thus forming a much warmer warm core. Full article

The aim of the present study is to analyze the triggering mechanisms of three thunderstorms (TSs) associated with severe rainfall, hail and lightening in the tropical central Andes of Peru, specifically above the Huancayo observatory (12.04 ${}^{\circ }$ S, 75.32 ${}^{\circ }$ W, 3313 m a.s.l.) located in the Mantaro valley during the spring-summer season (2015–2016). For this purpose, we used a set of in-situ pluviometric observations, satellite remote sensing data, the Compact Meteorological Ka-Band Cloud Radar (MIRA-35C), the Boundary Layer Tropospheric Radar and downscaling model simulations with the Weather Research and Forecasting (WRF) Model (resolutions: 18 km, 6 km and 2 km), and the Advance Regional Prediction System (ARPS) (resolution: 0.5 km) models in order to analyze the dynamic of the atmosphere in the synoptic, meso and local scales processes that control the occurrence of the three TS events. The results show that at synoptic scale, the TSs are characterized by the southern displacement of the South-east Pacific Subtropical Anticyclone up to latitudes higher than 35 ${}^{\circ }$ S, by the weakening and south-eastern displacement of the Bolivian high–North east low system and by the intrusion of westerly winds along the west side of the central Andes at upper and medium levels of the atmosphere. At meso-scale, apparently, two important moisture fluxes from opposite directions are filtered through the passes along the Andes: one from the north-west and the other from the south-east directions converge and trigger the deep convection into the Mantaro valley. These moisture fluxes are generated by the intrusion of the sea-breeze from the Pacific ocean along the west of the Andes coupling with upper and middle westerly winds and by the thermally induced moisture fluxes coming from the South American low level jet at the east side of the Andes. At the local scale, there is a low-level conditional instability in the previous hours as well as during the occurrence of the TSs above the Huancayo observatory. In addition, the simulation results indicated the possibility of generation of inertial gravity waves in the Amazon basin, associated with geostrophic adjustment which transports energy and moisture into the central Andes plateau and consequently intensifies the thunderstorms above the Mantaro valley. Full article

Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R² values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R² values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs. Full article

With high resolution and wide coverage, satellite precipitation products like Global Precipitation Measurement (GPM) could support hydrological/ecological research in the Tianshan Mountains, where the spatial heterogeneity of precipitation is high, but where rain gauges are sparse and unevenly distributed. Based on observations from 46 stations from 2014–2015, we evaluated the accuracies of three satellite precipitation products: GPM, Tropical Rainfall Measurement Mission (TRMM) 3B42, and the Climate Prediction Center morphing technique (CMORPH), in the Tianshan Mountains. The satellite estimates significantly correlated with the observations. They showed a northwest–southeast precipitation gradient that reflected the effects of large-scale circulations and a characteristic seasonal precipitation gradient that matched the observed regional precipitation pattern. With the highest correlation (R = 0.51), the lowest error (RMSE = 0.85 mm/day), and the smallest bias (1.27%), GPM outperformed TRMM and CMORPH in estimating daily precipitation. It performed the best at both regional and sub-regional scales and in low and mid-elevations. GPM had relatively balanced performances across all seasons, while CMORPH had significant biases in summer (46.43%) and winter (−22.93%), and TRMM performed extremely poorly in spring (R = 0.31; RMSE = 1.15 mm/day; bias = −20.29%). GPM also performed the best in detecting precipitation events, especially light and moderate precipitation, possibly due to the newly added Ka-band and high-frequency microwave channels. It successfully detected 62.09% of the precipitation events that exceeded 0.5 mm/day. However, its ability to estimate severe rainfall has not been improved as expected. Like other satellite products, GPM had the highest RMSE and bias in summer, suggesting limitations in its way of representing small-scale precipitation systems and isolated deep convection. It also underestimated the precipitation in high-elevation regions by 16%, suggesting the difficulties of capturing the orographic enhancement of rainfall associated with cap clouds and feeder–seeder cloud interactions over ridges. These findings suggest that GPM may outperform its predecessors in the mid-/high-latitude dryland, but not the tropical mountainous areas. With the advantage of high resolution and improved accuracy, the GPM creates new opportunities for understanding the precipitation pattern across the complex terrains of the Tianshan Mountains, and it could improve hydrological/ecological research in the area. Full article