Precipitation Radar Research Articles

Development of a Precipitation Climate Record from Spaceborne Precipitation Radar Data. Part II: Temporal Adjustment of the Calibration Change Using the Ocean Normalized Radar Cross Section

Abstract We analyze the calibration stability of the 17-yr precipitation radar (PR) data on board the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM, version 7, is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM, version 8 (or later), takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis. Significance Statement The stability of long-term data is very important for climate research so that an account of temporal calibration changes in the sensor must be made. In this study, we investigate the calibration stability of the TRMM PR data and evaluate its impact on the precipitation trend analysis. The temporal change of the PR calibration is estimated to be 0.19 dB decade−1. Compensating for this change improves the consistency of precipitation trend analysis between the PR and other precipitation datasets. The reprocessed PR data provide more probable data for long-term precipitation analysis.

Lightning activity in India an important cause of increase in fatalities

With Recent increase in deaths due to lightning activity in India we are posed with a question - are these fatalities related to increase in the lightning activity owing to climate change? The IPCC report (2013) projects global warming of 1-5 °C by the end of 21st Century. The global warming is closely related to increased concentration of greenhouse gases. Previous studies have shown that on different temporal and spatial scales small increases in surface temperature leads to increase in thunderstorm and lightning activity. The lightning induced deaths are on the rise especially in the tropical south Asia and Africa but IPCC report does not explicitly deals with lightning activity and its future projection. Studies on this aspect become all the more important as the subgrid scale phenomena such as convective clouds and hence lightning are poorly resolved and taken in to account by climate models. In order to address this issue we have analyzed trends of lightning activity, surface temperature, upper tropospheric water vapour, cloud ice, Convective Available Potential Energy (CAPE) and aerosols. We also present correlation of these parameters with lightning activity using lightning flash rate data of Lightning Imaging Sensor aboard TRMM, TRMM Level -2 Precipitation Radar data, gridded temperature data of IMD, aerosol data acquired by MODIS. The result indicates that upper tropospheric temperature rise is more than surface temperature rise. This imply stable atmosphere with fewer thunderstorms. The increased convection transports additional water vapour into upper troposphere. The water vapour acts as green house has by absorbing infrared radiation emitted by the surface of the Earth. This results in more warming in the upper troposphere than at the surface and stabilizing of the atmosphere. However, results also show that within the thunderstorm the instability measured by CAPE is positively correlated with lightning activity. This paradox of stabilization of global mean atmosphere with increase in lighting activity leads us to conclude that tough thunderstorm activity has subdued but those develop are much more explosive producing more lightning activity and perhaps lead to more fatalities.

Analyzing the heavy rainfall event of July 2011 in Niigata using ground, satellite and radar rainfalls

In recent years the application of radar and satellite precipitation observations has become increasingly useful in poorly gauged areas. However, understanding the relationships between these data sources and ground observations is vital to correct the datasets and improve their application in hydrological studies. In this study we analyzed the spatial and temporal relationships between Global Satellite Mapping of Precipitation-Near Real Time (GSMaP_NRT) data set with radar and ground observations at downstream of Shinano River, Japan. GSMap_NRT observation showed better relationship with ground observations for longer-duration observations (eg. 3, 6, 12 and 18 hours) than hourly observations. The GSMap_NRT showed excellent relationship with ground rainfall at satellite observation time compared to non-observation time. Comparison of radar observations at various time scales and spatial resolutions showed that radar estimates at smaller-time-interval ratio and lower-spatial-scale ratio are better than longer-time-interval ratio and higher-spatial-scale ratio. We also observed that radar precipitation estimate well-represent the areal averaged precipitation of ground observations. Results from this study showed that radar precipitation estimates could serve as very important input to improve merging of ground and satellite precipitation as well satellite rainfall improvement systems.

An Investigation on Microphysical Characteristics of Early‐, Late‐, and Post‐Mei‐yu Season Rainfall Over Taiwan

AbstractOver Taiwan, Mei‐yu season (May and June), which is primarily linked to frontal systems, is the transition period between winter and summer. Using the Global Precipitation Measurement Mission dual‐frequency precipitation radar (GPM DPR), the current study examined the rain and microphysical characteristics of the Mei‐yu season in Taiwan. In order to examine the areal and intra‐seasonal aspects, May and June months are divided into three sub‐seasons: early‐Mei‐yu, late‐Mei‐yu, and post‐Mei‐yu. The three sub‐seasons exhibited differences in rainfall and raindrop size distributions, with abundance of large drops in the post‐Mei‐yu. Additionally, there were noticeable variations in the raindrop size distributions among the south, central, north, and eastern parts of Taiwan, with more large drops in central Taiwan. To comprehend the microphysical progressions causing the regional and intra‐seasonal fluctuations, CFADs (contoured frequency by altitude diagrams) of rain parameters are utilized. Compared to other two sub‐seasons, the early‐Mei‐yu season exhibited weaker convection. Dominance of stratiform precipitation in late‐Mei‐yu season, and convective precipitation in post‐Mei‐yu season resulted in higher rainfall amounts in these two sub‐seasons (more particularly in post‐Mei‐yu) than the early‐Mei‐yu season. Examination of warm rain microphysical processes (below 5 km height) among these sub‐seasons revealed that the early‐Mei‐yu season is dominated with break‐up process, late‐Mei‐yu season with breakup and coalescence balance, and post‐Mei‐yu with coalescence, size‐sorting and evaporation processes.

Microphysical Characteristics of Monsoon Precipitation over Yangtze-and-Huai River Basin and South China: A Comparative Study from GPM DPR Observation

It is rare to conduct a comparative analysis of precipitation characteristics across regions based on long-term homogeneous active satellite observations. By collocating the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) observations with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5) data, this study comparatively examines the microphysics of monsoon precipitation in the rainy season over the Yangtze-and-Huai River Basin (YHRB) and South China (SC) from 2014 to 2023. The comparative analysis is made in terms of precipitation types and intensities, precipitation efficiency index (PEI), and ice phase layer (IPL) width. The results show that the mean near-surface precipitation rate and PEI are generally higher over SC (2.87 mm/h, 3.43 h−1) than over YHRB (2.27 mm/h, 3.22 h−1) due to the more frequent occurrence of convective precipitation. The DSD characteristics of heavy precipitation in the wet season for both regions are similar to those of deep ocean convection, which is associated with a greater amount of water vapor. However, over SC, there are larger but fewer raindrops in the near-surface precipitation. Moreover, moderate PEI precipitation is the main contributor to heavy precipitation (>8 mm/h). Stratiform precipitation over YHRB is frequent enough to contribute more than convective precipitation to heavy precipitation (8–20 mm/h). The combined effect of stronger convective available potential energy and low-level vertical wind favors intense convection over SC, resulting in a larger storm top height (STH) than that over YHRB. Consequently, it is conducive to enhancing the microphysical processes of the ice and melt phases within the precipitation. The vertical wind can also influence the liquid phase processes below the melting layer. Collectively, these dynamic microphysical processes are important in shaping the efficiency and intensity of precipitation.

Let It Snow: Intercomparison of Various Total and Snow Precipitation Data over the Tibetan Plateau

The Global Precipitation Measurement Mission (GPM) improved spaceborne precipitation data. The GPM dual-frequency precipitation radar (DPR) provides information on total precipitation (TP), snowfall precipitation (SF) and snowfall flags (surface snowfall flag (SSF) and phase near surface (PNS)), among other variables. Especially snowfall data were hardly validated. This study compares GPM DPR TP, SF and snowfall flags on the Tibetan Plateau (TiP) against TP and SF from six well-known model-based data sets used as ground truth: ERA 5, ERA 5 land, ERA Interim, MERRA 2, JRA 55 and HAR V2. The reanalysis data were checked for consistency. The results show overall high agreement in the cross-correlation with each other. The reanalysis data were compared to the GPM DPR snowfall flags, TP and SF. The intercomparison performs poorly for the GPM DPR snowfall flags (HSS = 0.06 for TP, HSS = 0.23 for SF), TP (HSS = 0.13) and SF (HSS = 0.31). Some studies proved temporal or spatial mismatches between spaceborne measurements and other data. We tested whether increasing the time lag of the reanalysis data (+/−three hours) or including the GPM DPR neighbor pixels (3 × 3 pixel window) improves the results. The intercomparison with the GPM DPR snowfall flags using the temporal adjustment improved the results significantly (HSS = 0.21 for TP, HSS = 0.41 for SF), whereas the spatial adjustment resulted only in small improvements (HSS = 0.12 for TP, HSS = 0.29 for SF). The intercomparison of the GPM DPR TP and SF was improved by temporal (HSS = 0.3 for TP, HSS = 0.48 for SF) and spatial adjustment (HSS = 0.35 for TP, HSS = 0.59 for SF).

A New Algorithm of Rain Type Classification for GPM Dual-Frequency Precipitation Radar in Summer Tibetan Plateau

A New Algorithm of Rain Type Classification for GPM Dual-Frequency Precipitation Radar in Summer Tibetan Plateau

Diurnal cycles of rain storms over South China during the presummer rainy season

The variation characteristics of rain storms (RSs) are closely related to complex internal physical processes and external dynamic and thermodynamic conditions. Using a 17-year observational rainfall dataset from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), the diurnal cycles of various RS characteristics and their responses to the South China Sea Summer Monsoon (SCSSM) are investigated in South China (SC) during the presummer rainy season (PSRS). Before SCSSM outbreaks, the RSs in the western inland region of SC (WSC) have stronger precipitation intensities and convective features due to orographic lifting, especially at night, while those in the eastern inland region of SC (ESC) have higher RS occurrence frequencies with higher moisture transport, especially in the afternoon. The onset of the SCSSM leads to abundant moisture and sufficient convective available potential energy over the entire SC area, resulting in significant increases in the occurrence frequency, convective fraction, and precipitation top height of RSs. In the WSC, the weakened upward motion and smaller convective inhibition limit RS development, resulting in weaker rainfall intensity. In the ESC, a particularly unstable atmosphere and enhanced low-level moisture transport in the afternoon lead to a larger three-dimensional size, more active convection, and greater precipitation intensity in the RSs. These results offer fresh insight into the diurnal variation in rainfall during the PSRS and can be effectively used to evaluate numerical model simulations.

Exploring patterns in precipitation intensity–duration–area–frequency relationships using weather radar data

Abstract. Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time; therefore, to better understand and manage the related risks, knowledge of their probability at different spatial–temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial–temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid, and arid areas across a few tens of kilometres, and its susceptibility to flash flood. At-site intensity–duration–area–frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25–500 km2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This finding has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial–temporal scales.

Short-Term Precipitation Radar Echo Extrapolation Method Based on the MS-DD3D-RSTN Network and STLoss Function.

Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficient capture of spatiotemporal dependencies, and low accuracy in radar echo extrapolation, we propose a new model: MS-DD3D-RSTN. This model employs spatiotemporal convolutional blocks (STCBs) as spatiotemporal feature extractors and uses the spatial-temporal loss (STLoss) function to learn intra-frame and inter-frame changes for end-to-end training, thereby capturing the spatiotemporal dependencies in radar echo signals. Experiments on the Sichuan dataset and the HKO-7 dataset show that the proposed model outperforms advanced models in terms of CSI and POD evaluation metrics. For 2 h forecasts with 20 dBZ and 30 dBZ reflectivity thresholds, the CSI metrics reached 0.538, 0.386, 0.485, and 0.198, respectively, representing the best levels among existing methods. The experiments demonstrate that the MS-DD3D-RSTN model enhances the ability to capture spatiotemporal dependencies, mitigates forecast degradation, and further improves radar echo prediction performance.

Validation of GPM DPR Rainfall and Drop Size Distributions Using Disdrometer Observations in the Western Mediterranean

Dual-frequency precipitation radar (DPR) on the Core GPM satellite provides spaceborne three-dimensional observations of precipitation fields and surface rainfall rate with quasi-global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and drop size distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR-derived products, version 07, from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground references. Four matching techniques between satellite estimates and ground level observations were tested, and the best results were found for the so-called optimal comparison approach. Overall, GPM DPR products captured the variability of the observed DSD well at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw were observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, the lowest errors were found for radar reflectivity factor and Dm, and the highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested, and a substantial overestimation of stratiform cases compared to disdrometer observations were found.

A Climatology of Convective Precipitation over Europe

Abstract The annual, seasonal, and diurnal spatiotemporal heavy convective precipitation patterns over a pan-European domain are analyzed in this study using a combination of datasets, including the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) precipitation rate product, E-OBS ground-based precipitation gauge data, European climatological gauge-adjusted radar precipitation dataset (EURADCLIM), Operational Programme for the Exchange of Weather Radar Information (OPERA) ground-based radar-derived precipitation rates, and fifth major global reanalysis produced by ECMWF (ERA5) total and convective precipitation products. Arrival Time Difference Network (ATDnet) lightning data are used in conjunction with IMERG and EURADCLIM precipitation rates, with an imposed threshold of 10 mm h−1 to classify precipitation as convective. Annually, the largest convective precipitation accumulations are over the European seas and coastlines. In summer, convective precipitation is more common over the European continent, though relatively large accumulations exist over the northern coastal waters and the southern seas, with a seasonal localized maximum over the northern Adriatic Sea. Activity shifts southward to the Mediterranean and its coastlines in autumn and winter, with maxima over the Ionian Sea, the eastern Adriatic Sea, and the adjacent coastline. Over the continent, 1%–10% of the total precipitation accumulated is classified as convective, increasing to 10%–40% over the surrounding seas. In contrast, 30%–50% of ERA5 precipitation accumulations over land is produced by the convective parameterization scheme and 40%–60% over the seas; however, only 1% of ERA5 convective precipitation accumulations are from rain rates exceeding 10 mm h−1. Regional analyses indicate that convective precipitation rates over the inland mountains follow diurnal heating, though little to no diurnal pattern exists in convective precipitation rates over the seas and coastal mountains.

Land-sea contrast of vertical structure of precipitation over Sumatra revealed by GPM DPR observations

This study examines the land-sea contrast in the vertical structure of precipitation over Sumatra and its surrounding ocean using eight years (2014–2021) of dual-frequency precipitation radar (DPR) data from the Global Precipitation Measurement (GPM) mission. The study area is segmented into Ocean, Coast I (west coast), Land I (west of Barisan Mountains), Land II (east of Barisan Mountains), and Coast II (east coast). The effective reflectivity factor (Ze), rainfall rate (R), and two raindrop size distribution (DSD) parameters, namely the mass-weighted mean diameter (Dm) and the normalized intercept (Nw), were analyzed. Stratiform and deep convective rainfall were the most frequent types in Coast I, followed by Ocean, Land I, Land II, and Coast II. In contrast, shallow convective rainfall was the most common type in Ocean, followed by Coast I, Land I, Land II, and Coast II. The average rain top height (RTH) was found to be higher in Land II and Coast II than in Ocean, Coast I, and Land I, in accordance with the surface rainfall intensity pattern previously reported by other studies. Furthermore, the data on heavy ice precipitation demonstrated an increase from the ocean to the coast and land, with a significant number of instances observed in Land I, Land II, and Coast II, in addition to Coast I. This contrast in heavy ice precipitation and RTH influences DSD. The land-sea contrast of DSD is more pronounced for deep convective rains. Deep convective exhibited a lower frequency of large raindrops over the ocean than over the coast and land, as reflected in the Dm profile. Conversely, raindrop concentration, particularly small drops, was higher at sea. The percentage of Dm > 2 mm at sea was approximately 2%, rising to 4–6% in Land II and Coast II. The land-sea contrast in the vertical structure of precipitation over Sumatra exhibited apparent diurnal variation. Larger Dm and smaller Nw were observed over Land I and Coast I, particularly in the afternoon and evening, correlating with peak rainfall. This pattern aligns with heavy ice precipitation profiles, vertical relative humidity (RH), and wind profiles. This study highlights the complexities and potential discrepancies between vertical precipitation profiles and surface precipitation data, underscoring the nuanced nature of land-sea precipitation migration.

A quest for precipitation attractors in weather radar archives

Abstract. Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fields. In the first approach, the phase space variables of the attractor are defined using the domain-scale statistics of precipitation fields, such as the mean precipitation, fraction of rain, and spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales and climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000×4000 km2, 21 years) and the Swiss Alpine region (≈500×500 km2, 6 years).

Seasonal variations in microphysics of convective and stratiform precipitation over North China revealed by GPM dual-frequency precipitation radar

Seasonal variations in microphysics of convective and stratiform precipitation over North China revealed by GPM dual-frequency precipitation radar

Microphysical characteristics of precipitation within convective overshooting over East China observed by GPM DPR and ERA5

Abstract. We examine the geographical distribution and microphysical three-dimensional structure of convective overshooting over East China by matching the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) instrument with the European Centre for Medium-Range Weather Forecasts Fifth-Generation Reanalysis (ERA5). Convective overshooting mainly occurs over Northeast China (NC) and northern Middle and East China (MEC), and its frequency varies from 4×10-4 to 5.4×10-3. Radar reflectivity of convective overshooting over NC accounts for a higher proportion below the freezing level, while MEC and South China (SC) account for a higher proportion above the freezing level, indicating stronger upward motion and more ice crystal particles. The microphysical processes within convective overshooting are unique, leading to various properties of the droplets in precipitation. Droplets of convective overshooting are large but sparse, with an effective droplet radius of nearly 2.5 mm below 10 km, which is about twice that of non-overshooting precipitation. The findings of this study may have important implications for the microphysical evolution associated with convective overshooting and provide more accurate precipitation microphysical parameters as input for model simulations.

Near‐Storm Environmental Relationships With Tropical Oceanic Convective Structure Observed During NASA CPEX and CPEX‐AW

AbstractDeep tropical oceanic convection (TOC) is a prevailing component of the tropical atmosphere and plays a significant role in modulating global weather and climate. Despite its importance, prediction challenges remain, partly attributed to a lack of understanding of how TOC relates to its near‐storm environments. Prior studies suggest location‐dependent relationships between TOC structure and associated environments, necessitating targeted regional studies. The NASA 2017 Convective Processes Experiment (CPEX) and 2021 CPEX—Aerosols & Winds (CPEX‐AW) field campaigns collected high‐resolution measurements of convective storms and their environments in the Gulf of Mexico, Caribbean, and western Atlantic basins, providing a rare opportunity to investigate near‐storm environmental relationships with 3‐D TOC structure where in situ non‐tropical cyclone‐related deep TOC research is comparatively lacking. Collocated CPEX and CPEX‐AW airborne observations from the multi‐wavelength Airborne Precipitation Radar, Doppler Aerosol Wind Lidar, and dropsondes revealed large near‐storm environmental variability across TOC of similar convective type (i.e., isolated, organized) and within individual convective systems. However, trends still emerged amongst the large environmental variability. Horizontal TOC structure was most consistently linked to planetary boundary layer and mid‐tropospheric near‐storm environments, with organized TOC being associated with generally greater relative humidity (RH) and vertical speed shear than isolated TOC. TOC intensity was linked to upper tropospheric (i.e., above melting level) near‐storm environments, with isolated TOC intensity most consistently associated with upper tropospheric CAPE and organized TOC intensity associated with upper tropospheric RH. Mesoscale low‐level convergence was also linked to greater organized TOC intensity, motivating further research using these unique data sets.

Evaluation of IMERG Data over Open Ocean Using Observations of Tropical Cyclones

The IMERG data product is an optimal combination of precipitation estimates from the Global Precipitation Mission (GPM), making use of a variety of data types, primarily data from various spaceborne passive instruments. Previous versions of the IMERG product have been extensively validated by comparisons with gauge data and ground-based radars over land. However, IMERG rain rates, especially sub-daily, over open ocean are less validated due to the scarcity of comparison data, particularly with the relatively new Version 07. To address this issue, we consider IMERG V07 30-min data acquired in tropical cyclones over open ocean. We perform two tasks. The first is a straightforward comparison between IMERG precipitation rates and those retrieved from the GPM Dual-frequency Precipitation Radar (DPR). From this, we find that IMERG and DPR are close at low rain rates, while, at high rain rates, IMERG tends to be lower than DPR. The second task is the assessment of IMERG’s ability to represent or detect structures commonly seen in tropical cyclones, including the annular structure and concentric eyewalls. For this, we operate on IMERG data with many machine learning algorithms and are able to achieve a 96% classification accuracy, indicating that IMERG does indeed contain TC structural information.

Geostationary Satellite-Based Overshooting Top Detections and Their Relationship to Severe Weather over Eastern China

Overshooting tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h−1) and extreme wind speed (e.g., ≥17.2 m s−1) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms.

Improving Explainability of Deep Learning for Polarimetric Radar Rainfall Estimation

AbstractMachine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities.