Precipitation Measurement Research Articles

Has IMERG_V07 Improved the Precision of Precipitation Retrieval in Mainland China Compared to IMERG_V06?

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG.

Pemodelan Debit Banjir Rencana Berbasis Data Hujan Satelit pada Daerah Tangkapan Air Bendungan Meninting

Rainfall stations around the Meninting Dam catchment area are very limited. Global Precipitation Measurement (GPM) satellite rainfall data can be utilized to fulfill the lack of rainfall data. This research aims to improve the quality of flood discharge in the Meninting Dam catchment area through rainfall data correction. GPM rainfall data was corrected using the probability curve method, while flood discharge analysis used HEC-HMS modeling. The results showed that the correlation coefficient of GPM rainfall data with Sesaot Rainfall Station was 0,73, the average HHMT error decreased by 2.99% after correction, and the correction factor level was 0-1,2 for each rainfall value range. The flood discharge (Q) of HEC-HMS modeling for each return period was obtained Q2th = 117,1 m3/sec, Q5th = 142,5 m3/sec, Q25th = 242,3 m3/sec, Q100th = 335,6 m3/sec, Q1000th = 529,3 m3/sec, and QPMF = 930,9 m3/sec.

Reconstructing high-resolution gridded precipitation data in the southwest China highland canyon area using an improved (MGWR) downscaling method

High-spatiotemporal-resolution rainfall data are vital for investigating local terrestrial water cycles. Although remote-sensing satellite retrieval of precipitation products effectively reproduces spatial patterns of rainfall, it suffers from low spatial resolution. To overcome such limitations, a two-step downscaling approach is proposed here. First, 80 % of the meteorological-station data is utilized to calibrate the original Global Precipitation Measurement (GPM) data, enhancing the correlation between GPM and station data. Subsequently, utilizing elevation, slope, aspect, the normalized difference vegetation index (NDVI), wind direction, water vapor, and land surface temperature, as well as slope and aspect correction factors, as independent variables, multiscale geographically weighted regression (MGWR) and temporal lag MGWR (TL-MGWR) models were constructed. Through the aforementioned steps, downscaled monthly and daily precipitation data for the geographic region under investigation in 2022 at a spatial resolution of 0.01° were obtained.Our findings indicate that selectively employing suitable MGWR or TL-MGWR models on a monthly basis can effectively downscale monthly GPM rainfall data. The downscaled (original) monthly precipitation data exhibited a correlation of 0.94 (0.768), with a mean absolute error (MAE) of 16.233 mm/month, root-mean-square error (RMSE) of 27.106 mm/month, and bias of −0.043. Similar enhancement was likewise noted in daily precipitation, displaying a correlation coefficient of 0.863 (0.318) for downscaled (original) data, and a RMSE of 3.209 mm/day, MAE of 1.082 mm/day, and bias of −0.06. The downscaled results show a correlation increase of 0.172 monthly and 0.545 daily, with MAE reductions of 18.43 mm/month and 1.658 mm/day, RMSE reductions of 26.172 mm/month and 4.183 mm/day, and bias reductions of 82.7 % and 56.8 %. In summary, the data after downscaling, both for monthly and daily datasets, was markedly improved in accuracy. The proposed downscaling method is applicable for reconstructing high-resolution grid data in the complex terrain of the southwest China highland canyon area.

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

AbstractPassive microwave radiometers onboard satellites rely on the received upwelling radiation to retrieve precipitation, which is a mixed signal from the surface, atmosphere and precipitation hydrometeors. Liquid precipitation droplets increase the upwelling radiation from the surface at lower frequencies, while ice particles cause a decrease in upwelling radiation at higher frequencies. The task of the retrieval algorithm is to identify the precipitation phase and to isolate the signal of precipitation from that of the surface. This study develops a machine learning method to retrieve rainfall and snowfall rates based on observations from the Microwave Hydrometer Sounder and Microwave Temperature Sounder onboard FY‐3E. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The half‐hour product of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) is used to train the ANN. To address the issue that number of heavy precipitation samples are not enough for training, the simulation of radiative transfer for TOVS is used as a supplement to heavy rain samples. The SOM‐ANN algorithm outperforms the IMERG and Goddard profiling algorithm (GPROF) retrieval products in both rainfall and snowfall retrieval. Compared with the hourly observations at ∼4,400 stations during a 2‐year period, the root mean square errors of SOM‐ANN proposed here are 1.06 and 0.34 mm/hr for the rainfall and snowfall rates, which are better than those of IMERG (1.23 and 0.42 mm/hr) and GPROF (1.22 and 0.44 mm/hr).

Extreme weather has variable effects on reproductive success of grassland songbirds at the northern extent of their range

Abstract Grassland songbirds breeding in Canada and the United States have experienced significant population declines likely because of habitat loss and degradation. Many climate change models predict an increase in the frequency, intensity, and duration of extreme precipitation and temperature events that could place further pressures on declining species. We monitored the fate of 1,868 individual nesting attempts of 7 grassland songbird species in response to various precipitation and temperature measures over a 10-yr period (1997 to 2002 and 2004 to 2008) in Saskatchewan, Canada. Daily nest survival rates of 5 species, including 3 at-risk species, were negatively influenced by high levels of precipitation, although the amount of precipitation where declines in daily nest survival occurred varied. Daily nest survival rates of 2 species were negatively correlated with high temperatures. We failed to detect any relationship between precipitation or temperature and the number of fledglings produced from successful nests. Extreme weather events could add additional stressors to declining populations of grassland birds in Canada. Increases in the frequency and intensity of extreme weather, specifically extreme precipitation events and short-term high temperatures, will likely lead to lower reproductive success for several species compared to current levels. This may be especially problematic for management of Anthus spragueii (Sprague’s Pipit) and Centronyx bairdii (Baird’s Sparrow), where a large proportion (> 75%) of the breeding population occurs near the northern edge of the Great Plains. The continuing loss and degradation of northern grasslands may limit the ability of these species to disperse and find favorable climate conditions.

Pre-processing satellite rainfall products improves hydrological simulations with machine learning

ABSTRACT A new pre-processing methodology for gridded Satellite Precipitation Products (SPPs) is developed to improve the performance of Machine Learning (ML) algorithms for runoff prediction. The developed approach was applied to capture the rainfall patterns, and to select relevant input data. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modelling. The methodology was tested in a semiarid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPMIMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. The results demonstrate the effectiveness of the proposed approach using all employed SPPs. In terms of Nash-Sutcliffe efficiency, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and of ELM by 15%, which highlights how pre-processing techniques significantly enhance ML models with SPP data.

Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products

Reanalysis and satellite-based rainfall packages are useful for monitoring hydroclimatic extremes. These advanced tools can be used as an early-warning system for decision-making during extreme events. Hurricane Dorian and Fiona impacted the North Atlantic from September 6–9, 2019 and September 22–25, 2022, respectively. This study evaluated the Canadian Precipitation Analysis (CaPA) in conjunction with integrated multi-satellite retrievals for the Global Precipitation Measurement Mission (GPM) in capturing these events at Early, Late and Final run stages (IMERG-E, IMERG-L & IMERG-F, respectively). Statistical verification was conducted through continuous, categorical and spatial methods to answer multiple research questions. The results show that CaPA outperformed all the IMERG products across various intensities throughout the duration of the extremes compared to station observations; therefore, CaPA can be a proxy for gauged observations. When CaPA was used as the reference for spatial verification, IMERG products correctly captured the spatial evolution of the hurricane from day to day, indicating they are reliable for hydroclimatic extreme applications. In addition, there are no striking statistical differences between the IMERG products despite the fact that IMERG-E and IMERG-L had no adjustment or gauge observation. In general, this study shows that IMERG-E has many potential applications such as CaPA assimilation, a hazard early warning system, and hurricane tracking and prediction. Further, including IMERG-E into CaPA will leverage its high resolution and latency. This study will benefit those involved in hydroclimatic studies and decision-making in the North Atlantic.

Improved Energy Resolution Measurements of Electron Precipitation Observed During an IPDP‐Type EMIC Event

AbstractHigh energy resolution DEMETER satellite observations from the Instrument for the Detection of Particle (IDP) are analyzed during an electromagnetic ion cyclotron (EMIC)‐induced electron precipitation event. Analysis of an Interval Pulsation with Diminishing Periods (IPDP)‐type EMIC wave event, using combined satellite observations to correct for incident proton contamination, detected an energy precipitation spectrum ranging from ∼150 keV to ∼1.5 MeV. While inconsistent with many theoretical predictions of >1 MeV EMIC‐induced electron precipitation, the finding is consistent with an increasing number of experimentally observed events detected using lower resolution integral channel measurements on the POES, FIREBIRD, and ELFIN satellites. Revised and improved DEMETER differential energy fluxes, after correction for incident proton contamination shows that they agree to within 40% in peak flux magnitude, and 85 keV (within 40%) for the energy at which the peak occurred as calculated from POES integral channel electron precipitation measurements. This work shows that a subset of EMIC waves found close to the plasmapause, that is, IPDP‐type rising tone events, can produce electron precipitation with peak energies substantially below 1 MeV. The rising tone features of IPDP EMIC waves, along with the association with the high cold plasma density regime, and the rapidly varying electron density gradients of the plasmapause may be an important factor in the generation of such low energy precipitation, co‐incident with a high energy tail. Our work highlights the importance of undertaking proton contamination correction when using the high‐resolution DEMETER particle measurements to investigate EMIC‐driven electron precipitation.

Evaluating Precipitation Events Using GPM IMERG 30-Minute Near-Real-Time Precipitation Estimates

Abstract Accurately detecting and estimating precipitation at near–real time (NRT) is of utmost importance for the early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), provides NRT 0.1° and 30-min precipitation estimates across the globe with only a 4-h latency. This study was an evaluation of the GPM IMERG version 6 level-3 early run 30-min precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-min precipitation from gauges and 30-min precipitation from the IMERG multisatellite constellation. False-positive and false-negative precipitation events were identified and analyzed to determine their characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months, suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged passive microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG–estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards. Significance Statement Accurately estimating rainfall to detect and monitor a precipitation event at near–real time is of utmost importance for hydrometeorological hazards. We used a state-of-the-art rainfall estimation product called GPM IMERG that uses infrared and passive microwave measurements collected from a constellation of satellites to produce near-real-time rainfall estimates every 30 min worldwide. The purpose of our research was to identify when, where, and why GPM IMERG falsely detected and missed precipitation events. Our results suggest that the frequency and timing of passive microwave precipitation with forward propagation were responsible for IMERG missing events, overestimating total rainfall during some precipitation events, and underestimating total rainfall in other precipitation events. Our future work will further investigate precipitation events using the GPM IMERG version 7 near-real-time product.

Bias Adjustment of Long‐Term (1961–2020) Daily Precipitation for China

AbstractThe observation errors in precipitation gauges contribute to diminished precision in precipitation data sets. To reduce the impact of these errors, the World Meteorological Organization Solid Precipitation Intercomparison Experiments recommended the Double Fence Intercomparison Reference as a reference standard for precipitation measurements. This study proposed a new rain, snow, and mixed precipitation adjustment method for national standard precipitation gauges, using DFIR‐measured precipitation as the standard values. This method was used to adjust for systematic errors, including wind‐induced errors, wetting loss, and trace precipitation, in precipitation data collected by 785 stations in China from 1961 to 2020. After bias adjustment, the annual precipitation increased by 6.1–177.9 mm (with an average of 52.7 mm), accounting for 3.3%–52.1% (8.9%) of the total precipitation. The average annual error‐adjustment amounts for wind‐induced error, wetting loss, and trace precipitation were 21.9 (3.6% of total precipitation), 26.6 (4.7%), and 4.2 mm (1.3%), respectively. The adjustment percentage in winter was higher than that in summer, with the high‐adjusted‐percentage regions predominantly located in areas with drought, high proportion of snowfall, and strong wind speeds. Additionally, the annual average error‐adjustment amounts for rain, snow, and mixed precipitation respectively accounted for 5.2%, 38.2%, and 17.1% of their corresponding total amounts, indicating the significance of bias adjustment, particularly for snow and mixed precipitation, in the northern and Qinghai‐Tibet Plateau regions. Therefore, bias adjustment is necessary to enhance the accuracy of the precipitation data set in China.

Impact of Assimilating Radar Solid Precipitation Data in the Canadian Precipitation Analysis System

Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

Remote sensing of electron precipitation mechanisms enabled by ELFIN mission operations and ADCS

The Electron Loss and Fields INvestigation (ELFIN) mission comprising two 3U+ CubeSats was developed, built, and operated by several generations of undergraduate students at UCLA. The spin-stabilized CubeSats (spin-rate: 21 RPM) produced high-resolution measurements of precipitating, trapped, and backscattered fluxes of electrons and ions in the radiation belts. Launched in September 2018, ELFIN operated successfully until its deorbit just over four years later. Initially, however, mission operations was very challenging and only tapped the full mission potential after a thorough redesign of the operations paradigm. This mid-mission adjustment yielded the higher data downlink volume necessary to acquire a comprehensive data set, therefore enabling ensemble studies with sufficient statistical significance. The new operational framework also led to additional improvements across the mission. Most notably, the Attitude Determination and Control System (ADCS) benefited from more reliable collections of magnetometer data for attitude determination, enabling knowledge and control to <1°. This allowed high pitch-angle resolution (especially within the loss cone) and high energy resolution spectrograms, both powerful diagnostics of radiation belt electron and ion precipitation. This paper highlights the scientific advancements made possible by ELFIN’s efficient mission operations and ADCS design, unique for CubeSats, and emphasizes the role of electron precipitation measurements for future studies in magnetospheric, ionospheric, and atmospheric physics.

Hydrometeor Identification Using GMI Passive Microwave Brightness Temperatures

Abstract Using passive microwave brightness temperatures Tb from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and hydrometeor identification (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of Tb. Eight years of collocated Tb and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015–20 used for training and 2021–22 used for testing the GMI-based HID retrieval. The occurrence of profiles with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of profiles predicted is highly correlated with the percentage observed (0.98 correlation coefficient for hail and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain profiles is less than snow for all Tb.

Prediction of Tropical Pacific Rain Rates with Overparameterized Neural Networks

Abstract The prediction of tropical rain rates from atmospheric profiles poses significant challenges, mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study introduces overparameterized neural networks not only to forecast tropical rain rates but also to explain their heavy-tailed distribution. The investigation is separately conducted for three rain types (stratiform, deep convective, and shallow convective) observed by the Global Precipitation Measurement satellite radar over the west and east Pacific regions. Atmospheric profiles of humidity, temperature, and zonal and meridional winds from the MERRA-2 reanalysis are considered as features. Although overparameterized neural networks are well known for their “double descent phenomenon,” little has been explored about their applicability to climate data and capability of capturing the tail behavior of data. In our results, overparameterized neural networks accurately estimate the rain-rate distributions and outperform other machine learning methods. Spatial maps show that overparameterized neural networks also successfully describe the spatial patterns of each rain type across the tropical Pacific. In addition, we assess the feature importance for each overparameterized neural network to provide insight into the key factors driving the predictions, with low-level humidity and temperature variables being the overall most important. These findings highlight the capability of overparameterized neural networks in predicting the distribution of the rain rate and explaining extreme values. Significance Statement This study aims to introduce the capability of overparameterized neural networks, a type of neural network with more parameters than data points, in predicting the distribution of tropical rain rates from gridscale environmental variables and explaining their tail behavior. Rainfall prediction has been a topic of importance, yet it remains a challenging problem for its heavy-tailed nature. Overparameterized neural networks correctly captured rain-rate distributions and the spatial patterns and heterogeneity of the observed rain rates for multiple rain types, which could not be achieved by any other previous statistical or machine learning frameworks. We find that overparameterized neural networks can play a key role in general prediction tasks, with potential expanded applicability to other domains with heavy-tailed data distribution.

A dataset of downscaled monthly precipitation at 1 km resolution in Guangxi, China and ASEAN regions from 2001 to 2020

Located in the southeastern Asia, Guangxi, China and the ASEAN regions have a subtropical monsoon climate with abundant rainfall and frequent floods, which has a great impact on social and economic activities. The precipitation data with high precision and high spatial and temporal resolution are of great significance for industrial and agricultural production, water conservancy development, drought and flood monitoring and prevention, and ecological environment protection. In this paper, we used the Global Precipitation Measurement Mission precipitation data (GPM IMERG) from 2001 to 2020 as the dependent variable in combination with the enhanced vegetation index (EVI), surface evapotranspiration (ET), land surface temperature (LST) from MODIS data and elevation (ELV) from ASTER data as explanatory variables. Then we introduced a geographically weighted regression (GWR) model to construct an annual scale model, depicting the spatial variation of 10-km satellite precipitation with the influence of geographical environmental conditions. Five types of kernel functions were adopted in our GWR model, including the gaussian, exponential, bisquare, tricube, and boxcar kernel function. The optimal kernel function was selected based on the correlation coefficient, the root means square error and bias. Thus, we established the dataset of downscaled monthly precipitation in Guangxi, China and ASEAN regions from 2001 to 2020 by extrapolating the GWR model with input variables at 1 km resolution. The dataset of 1-km monthly precipitation from 2001 to 2020 was also generated by the proportional coefficient method. Moreover, ground observation data from 2,679 stations during 2001–2020 were used for verification. The correlation coefficient, root mean square error and deviation were 0.792, 74.610mm and -0.122%, respectively. The results show that this dataset can reflect the precipitation spatial and temporal distribution and its variations at 1-km resolution in detail, and could be potentially promising for ecological environment, hydrological management, flood prediction and other relevant fields.

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.

The influence of hillslope topography on beech water use: a comparative study in two different climates

Abstract. Understanding the interrelation between topography and vegetation across different environments is important to assess how hydrological and climatic conditions affect tree physiological activity. This becomes especially important given the expected reduction in water availability and the increase in water demand driven by climate change. These extremes could enhance the thermal and hydrologic gradients along slopes. Here, we aimed to test if and how different climatic and hydrological conditions affect the physiological response of beech trees (Fagus sylvatica L.) to environmental variables along two different topographic sequences. For this purpose, we set up a comparative study on a gentle hillslope in the Weierbach catchment in Luxembourg (oceanic climate) and on a steep hillslope in the Lecciona catchment in Italy (Mediterranean climate). We combined sap velocity measurements with isotopic measurements of soil, precipitation, stream water, groundwater, and xylem over 2019 and 2020 for the Luxembourgish site and over 2021 for the Italian site. We found that, in the Weierbach catchment, trees' responses to environmental variables (i.e. vapour pressure deficit and relative extractable water in the soil) were similar among hillslope positions and between the two monitored years, resulting from homogeneous growing conditions along the topographic sequence. We also did not find any statistical difference in the isotopic composition of xylem water between positions, suggesting that beech trees relied on similar water sources across the landscape. In the Lecciona catchment, we observed lower sap velocities and shorter growing season in trees growing in the upper portions of the hillslope, likely related to water redistribution and different soil moisture along the hillslope catena. Xylem isotopic composition was significantly lighter at the footslope location throughout the growing season than in the upslope locations, suggesting location-specific water use. These results emphasize how differing hydrometeorological processes occurring at the hillslope scale can lead to contrasting tree responses.

Evaluation of GPM IMERG Satellite Precipitation Products in Event-Based Flood Modeling over the Sunshui River Basin in Southwestern China

This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas.

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.

Time Series Analysis: Associations Between Temperature and Primary Care Utilization in Philadelphia, Pennsylvania

IntroductionEarth's temperature has risen by an average of 0.11°Fahrenheit per decade since 1850 and experts predict continued global warming. Studies have shown that exposure to extreme temperatures is associated with adverse health outcomes. Missed primary care visits can lead to incomplete preventive health screenings and unmanaged chronic diseases. This study examines the associations between extreme temperature conditions and primary care utilization among adult Philadelphians. Methods1,048,575 appointments from 91,580 patients age ≥ 18 years enrolled in the study at thirteen university-based outpatient clinics in Philadelphia from January 1, 2009, to December 31, 2019. Statistical analysis was performed from June to December 2023. Data on attended and missed appointments was linked with measures of daily maximum temperature and precipitation, stratified by warm and cold seasons. Sociodemographic variables and associations with chronic disease status were explored. ResultsRates of missed appointments increased by 0.72% for every 1°F decrease in daily maximum temperatures below 39°F and increased by 0.64% for every 1°F increase above 89°F. Individuals≥65 years and those with chronic conditions had stronger associations with an increased rate of missed appointments. ConclusionsTemperature extremes were associated with higher rates of missed primary care appointments. Individuals with chronic diseases were more likely to have missed appointments associated with extreme temperatures. The findings suggest the need for primary care physicians to explore different modes of care delivery to support vulnerable populations, such as making telemedicine during extreme weather events a viable and affordable option.