Global Precipitation Measurement Satellite Research Articles

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.

Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees

Obtaining precise and detailed precipitation data is crucial for analyzing watershed hydrology, ensuring sustainable water resource management, and monitoring events such as floods and droughts. Due to the complex relationship between precipitation and geographic factors, this study divides the entire country of China into eight vegetation zones based on different vegetation types. Within each vegetation zone, we employ a seasonally adjusted Extreme Random Trees approach to spatially downscale GPM (Global Precipitation Measurement) satellite monthly precipitation data. To validate the effectiveness of this method, we compare it with kriging interpolation and traditional global downscaling methods. By increasing the spatial resolution of the GPM monthly precipitation dataset from 0.1° to 0.01°, we evaluate the downscaled results and validate them against ground-level rain gauge data and GPM satellite precipitation data. The results indicate that the partitioned area prediction method outperforms other approaches, resulting in a precipitation dataset that not only achieves high accuracy but also offers finer spatial resolution compared to the original GPM precipitation dataset. Overall, this approach enhances the model’s capability to capture complex spatial features and demonstrates excellent generalization. The resulting higher-resolution precipitation dataset enables the creation of more accurate precipitation distribution maps, providing data support for regions lacking hydrological information. These data can be used to analyze seasonal precipitation patterns and reveal differences in precipitation across different seasons and geographic regions.

Rainfall Variation on the West Coast of Sumatra during Each Phase of the Madden-Julian Oscillation from GPM Satellite Observation

The West Coast of Sumatra has a significantly higher annual rainfall than its surrounding areas, making this region crucial in controlling precipitation in Sumatra. The Madden-Julian Oscillation (MJO) heavily influences the West Coast of Sumatra. MJO is an intraseasonal activity that occurs in tropical regions and can be recognized by the eastward movement of convective activity from the Indian Ocean towards the Pacific Ocean for 30-60 days. Using Global Precipitation Measurement (GPM) satellite data from 2014 to 2020, this study investigates the influence of MJO on rainfall on the West Coast of Sumatra. The data used in this study is the attenuation-corrected reflectivity (dBZ) of Ku PR (Z-Ku) and Ka PR (Z-Ka) from GPM DPR Level 2 data. For convective rainfall, the Z-Ku and Z-Ka reflectivity values are higher during the inactive MJO phase than the active MJO phase, consistent with previous research in Sumatra. Thus, during the inactive MJO phase, strong convection occurs, leading to updrafts and increased formation of larger raindrops through coalescence processes. During the active MJO phase, there is a significant decrease in raindrop size, indicating dominant break-up processes.

Intercomparison between Ground-Based and Spaceborne Radars’ Echo-Top Heights: Application to the Multi-Radar Multi-Sensor and the Global Precipitation Measurement

Abstract The accuracy and uncertainty of radar echo-top heights estimated by ground-based radars remain largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. Because the vantage point of space is more suited than that of ground-based radars for the estimation of echo-top heights, the use of spaceborne radar observations is explored as an external reference for cross comparison. An investigation has been carried out across the conterminous United States by comparing the NOAA/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system with the space-based radar on board the NASA–JAXA Global Precipitation Measurement satellite platform. No major bias was assessed between the two products. An annual cycle of differences is found, driven by an underestimation of the stratiform cloud echo-top heights and an overestimation of the convective ones. The investigation of the systematic biases for different radar volume coverage patterns (VCP) shows that scanning strategies with fewer tilts and greater voids as VCP 21/121/221 contribute to overestimations observed for high MRMS tops. For VCP 12/212, the automated volume scan evaluation and termination (AVSET) function increases the radar cone of silence, causing overestimations when the echo top lies above the highest elevation scan. However, it seems that for low echo tops the shorter refresh rates contribute to mitigate underestimations, especially in stratiform cases.

Seasonal variations in precipitation microphysics over East China based on GPM DPR observations

The disdrometer-observed raindrop size distribution (DSD) in East China has been shown to vary across seasons and rainfall types. In this study, as an extensive research, the seasonal variations of vertical structures and associated microphysical processes of precipitation were investigated using nine-year measurements of dual-frequency precipitation radar on board the Global Precipitation Measurement satellite. The spatial distribution and vertical profiles of the reflectivity factor (Ze), mass-weighted mean diameter (Dm), and generalized intercept parameter (Nw) showed different magnitudes of variability among seasons (and rain types). Generally, the collisional-coalescence process was dominant for sufficient raindrop growth in convective precipitation, followed by the breakup of melted large ice particles. Summer convective precipitation exhibited typical monsoonal features with the largest Ze and Dm values, whereas extremely weak winter convective precipitation exhibited stratiform-like characteristics. For stratiform precipitation, the major microphysical processes were competitive between the breakup and coalescence processes during different seasons. In contrast, collisional coalescence was predominant in the shallow convective precipitation throughout the year. The distribution of rainfall and DSD parameters with respect to storm top height also varied. Specifically, the storm (convective core) top converged around the upper (middle) troposphere during extreme rainfall, indicating moderate convection where Dm reached its maximum with moderate log10Nw. Warm-rain processes also play an important role in raindrop growth. Moreover, the discrepancies between the extreme rainfall and the highest echo top suggest a weak linkage between the heaviest rainfall and tallest storms. This study provides a comprehensive picture of the seasonal variability in precipitation microphysics in East China.

Assessment of GPM Satellite Precipitation Performance after Bias Correction, for Hydrological Modeling in a Semi-Arid Watershed (High Atlas Mountain, Morocco)

Due to its important spatiotemporal variability, accurate rainfall monitoring is one of the most difficult issues in semi-arid mountainous environments. Moreover, due to the inconsistent distribution of gauge measurement, the availability of precipitation data is not always secured and totally reliable at the instantaneous time step. As a result, earth observation of precipitation estimations could be an alternative for overcoming this restriction. The current study presents a framework for either the hydro-statistical evaluation and bias correction of the Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals version 06 Early (IMERG-E), Late (IMERG-L), and Final (IMERG-F) products. On a sub-daily duration, from the Taferiat rain gauge-based station, which was used as a benchmark from 1 September 2014 to 31 August 2018. Statistical analysis was performed to examine each precipitation product’s performance. The results showed that all Post_Real_Time and Real_Time IMERG had a high level of awareness accuracy. The IMERG-L results were statistically similar to the gauge data, succeeded by the IMERG-F and IMERG-E. The Cumulative Distribution Function (CDF) has been employed to adjust the precipitation values of the three IMERG products in order to decrease bias estimation. The three products were then integrated into the “HEC-HMS” hydrological model to assess their dependability in flow modeling. Six flood occurrences were calibrated and validated for each product at 30-minute time steps. With a mean Nash-Sutcliffe coefficient of NSE 0.82, the calibration findings demonstrate that IMERG-F provides satisfactory hydrological performance. With an NSE = 0.80, IMERG-L displayed good hydrological utility, slightly better than IMERG-E with an NSE = 0.77. However, when the flood events were validated using the initial soil conditions, IMERG F and IMERG E overestimated the discharge by 13% and 10%, respectively. While IMERG L passed the validation phase with an average score of NSE = 0.69.

Analysis of Satellite Rain Data UsageOn The Rationalization Activities of The Rain Post Network (Case Study: Rationalization of The Jelai Watershed Rain Post Network)

An alternative solution to the availability of inadequate rain data as hydrological data input is with the help of Global Precipitation Measurement (GPM) satellite rainfall data using remote sensing technology (satellite). The purpose of this study was to find correlations and corrections of data and validate GPM satellite data with rainfall data at rain stations and observation data in the Jelai watershed. The corrected GP M rain data validation results in Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Correlation Coefficient (R), and Relative Error (KR). The validation results resulted in NSE values of 0.33, RMSE 48.54, Correlation Coefficient (R) of 0.75, and Relative Error of 0.19 for 2019 and yielded NSE values of -0.14, RMSE 100.24, Correlation Coefficient (R) of -0.36, and Relative Error of 0.23 for 2020. The overall analysis shows that GPM data can be used as an alternative to rain data if in a watershed there is a small number of rain posts that do not meet the WMO criteria. As a suggestion for further research, it is necessary to calibrate and validate by distinguishing between rain data in wet years and dry years

Validation of Rain Thickness Model Based on GPM Satellite and Surface Measurements

The limited acquisition of rainfall data from the Climatological Station of Deli Serdang has resulted in constraints on research in several fields. Global Precipitation Measurement (GPM) has spatial and temporal advantages as a solution to this problem. This study aims to provide information on the rain thickness equation model based on the Global Precipitation Measurement (GPM) Satellite as an alternative to calculating rain thickness at the Climatological Station of Deli Serdang. Data from the Global Precipitation Measurement (GPM) Satellite and Climatological Station of Deli Serdang were used, covering the period from January 1 to December 31, 2021. The Rescaled Adjusted Partial Sums (RAPS) method was used to verify the data quality, and the two data were consistent. The rain thickness equation model was obtained using a simple linear regression method. At the same time, the validation of the rain thickness equation model with surface measurements used the Root Mean Square Error (RMSE), Nash Sutcliffe Efficiency (NSE), and Correlation Coefficient (R) methods. The validation findings demonstrate that the rain thickness data from the Global Precipitation Measurement (GPM) Satellite was close to that of the Climatological Station of Deli Serdang, with an RMSE value of 5.85 mm, NSE of 0.99 mm, and R of 0.99. Hi = 5.21ti0.50 mm was the model used to calculate the equation for rain thickness. 6.84 mm, 0.69 mm, and 0.94 mm were the validation results of the equation model for RMSE, NSE, and R.Keywords: Rain Thickness, Global Precipitation Measurement (GPM), Climatological Station of Deli Serdang, Equation Model, Validation

Characteristics and physical mechanisms of a rainstorm in Hotan, Xinjiang, China

Owing to global warming, extreme precipitation events in the arid regions of Central Asia have increased, resulting in significant consequences for water resources and ecosystems. Hence, to address the features and corresponding physical mechanisms of these rainstorms, we examined the rainstorm that occurred in Hotan, Xinjiang in June 2021 as a case study. We employed multiple datasets, including meteorological stations, sounding observations, satellite precipitation data, and reanalysis datasets. The results indicate that the Global Precipitation Measurement satellite precipitation product accurately captured the temporal and spatial variations in this rainstorm, as verified against hourly in situ observation data. Some meteorological stations recorded values greater than twice their historical records, such as Luopu, Pishan, Moyu, and Hotan. Moreover, the duration of the precipitation was longer than 2 days. For the physical mechanisms of this rainstorm, the water vapor in this rainstorm is sourced from the 45°–65°N region of the North Atlantic Ocean crosses the Ural Mountains and the West Siberian Plain to southern Xinjiang. The low-pressure levels (e.g., 700 hPa and 850 hPa) have the more water vapor flux and specific humidity than the high-pressure levels. Our findings can aid the understanding of extreme precipitation events in Central Asia and provide a reference for dealing with meteorological disasters, including extreme precipitation, in the context of global climate change.

Ground validation of Fengyun-4A and Global Precipitation Measurement satellite observations over an alpine and canyon basin of the southeastern Tibetan Plateau

Ground validation of Fengyun-4A and Global Precipitation Measurement satellite observations over an alpine and canyon basin of the southeastern Tibetan Plateau

Evaluation of the Spatiotemporal Distribution of Precipitation Using 28 Precipitation Indices and 4 IMERG Datasets over Nepal

Accurate accounting of spatiotemporal variability of precipitation is essential for understanding the changing climate. Among the available precipitation estimates, the Global Precipitation Measurement (GPM) is an international satellite network providing advanced global precipitation estimates. The integrated multi-satellite retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation and yields a better performance in detecting precipitation events and spatial resolution. Here, we used twenty years (2001–2020) of IMERG Final data over the entire Nepal to analyze the spatial and temporal distribution of precipitation. This study evaluates the dynamic characteristics of the precipitation amounts, intensities, frequencies, and other relevant data across Nepal, using four IMERG datasets: (i) microwave only, (ii) infrared only, (iii) multi satellites gauge uncalibrated, and (iv) multi satellites gauge calibrated. A total of 28 precipitation indices was computed: threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, proportion-based indices, and additional seasonal indices. Results show that all four IMERG datasets are promising in capturing spatial details. The frequency of wet days corresponds with ground-based precipitation. Still, most indices, including consecutive wet days, annual and monsoon precipitation, and days when precipitation equaled or exceeded 20 and 50 mm, were substantially underestimated. In addition, the microwave-only dataset highly underestimated the precipitation amount. Notably, a substantial proportion of false alarms is a problem for all four IMERG datasets. Moreover, our results demonstrate that the IMERG uncalibrated dataset tends to overestimate precipitation during heavy precipitation events. These advantages and shortcomings of IMERG datasets over the rugged terrain of Nepal can provide useful feedback for sensor and algorithm developers to overcome limitations and improve retrieval algorithms. The study findings are helpful to the broader data users and practitioners for effective water decision applications.

On the Quality Control of HY-2 Scatterometer High Winds

The operational wind processor for the Ku-band scatterometers onboard HY-2 satellite series uses a quality control (QC) scheme based on the maximum likelihood estimator (MLE). Since it is difficult to discriminate rain contamination from “true” high winds, the MLE-based wind QC is set in a conservative way, which rejects up to ~35% of high winds (w ≥ 20 m/s) in HY-2 scatterometers (HSCATs). In this paper, the sensitivity of MLE and its spatially averaged value (i.e., MLEm) to wind quality and rain is reconsidered by analyzing the collocated HSCAT observations and buoy data, as well as rain data from the global precipitation measurement satellite’s microwave imagers. It shows that MLEm is more effective than MLE in terms of flagging rain data. More interestingly, the HSCAT high winds are much less strongly affected by rain, compared to the prior Ku-band pencil-beam scatterometers (e.g., RapidScat). Consequently, a MLEm-based approach is proposed to improve the HSCAT wind QC, particularly for high winds. The new QC method results in ~8% rejections at 20 m/s and above. Compared to the collocated buoy winds, the HSCAT high winds preserved by the new QC (but rejected by the operational QC) are of fairly good quality.

Improving the WRF Forecast of Landfalling Tropical Cyclones Over the Asia‐Pacific Region by Constraining the Cloud Microphysics Model With GPM Observations

AbstractWe proposed a method to improve the forecasts of landfalling tropical cyclones (LTCs) by constraining the “cloud physics” with Global Precipitation Measurement (GPM) satellite observations. Eight typical LTCs that are well observed by GPM satellite in the Asia‐Pacific region from 2015 to 2021 are selected to verify the feasibility of this method. Using a cloud‐resolving model, the LTCs are simulated for 3 days with both the original and modified microphysics scheme for comparison. The improvement of LTC forecasts is evaluated in terms of hydrometeor structure, amplitude, and location. Most notably, the structure forecast of condensed water improved up to 32% on average for all LTCs. The location forecast and amplitude forecast of condensed water also improved to varying degrees. Moreover, it is found that the error of LTC forecasts was reduced even more by using microphysics constraints from GPM observation than that by assimilating GPM data directly in other research.

GPM Ground Validation at NASA Wallops Precipitation Research Facility

Abstract The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, Virginia, has been established as a semipermanent supersite for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research-quality precipitation instruments, including NASA’s S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual-Frequency Precipitation Radar (DPR), combined DPR–GPM Microwave Imager (GMI) and GMI level II precipitation retrievals compared to WPRF ground observations from a 6-yr collection of satellite overpasses. Multisensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (Dm), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 min after overpass time but are not consistent between cases. In addition, GPM Core Observatory Dm retrievals are within level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (<1.0 mm h−1). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multisensor DPR/GMI combined algorithms outperform single-sensor DPR algorithm. Significance Statement Satellites are beneficial for global precipitation surveillance because extensive ground instruments are lacking, especially over oceans. Ground validation studies are required to calibrate and improve precipitation algorithms from satellite sensors. The primary goal of this study is to quantify the differences between satellite raindrop size and rain-rate retrieval with ground-based observations. Rainfall-rate algorithms require assumptions about the mean raindrop size. Results indicate Global Precipitation Measurement (GPM)/satellite-based mean raindrop size is within acceptable error (±0.5 mm) with respect to ground measurements. In addition, GPM satellite measurements overestimate light rain (<1.0 mm h−1), which is important during the winter months and at high latitudes. Illuminating the challenges of GPM satellite-based precipitation estimation can guide algorithm developers to improve retrievals.

Estimation of Extreme Rainfall over Kalimantan Island based on GPM IMERG Daily Data

Rainfall is one of the critical data for water resources infrastructure planning. In many cases in developing countries such as Indonesia, rainfall stations are not evenly distributed. In many cases, regional development occurs much faster than the improvement of hydrological measurement instruments. The plan to move the capital city of Indonesia to Kalimantan is one example. Satellites rainfall products can be utilized, especially for areas with a limited number of rainfall stations. This study examines the potential use of Global Precipitation Measurement (GPM) satellite products to estimate the spatial distribution of rainfall in the Kalimantan region. Twenty years data of daily maximum rainfall from GPM satellite rainfall products in 2001-2020 were compared to twenty years data of daily maximum rainfall from 16 rainfall stations under the Meteorology, Climatology, and Geophysical Agency (BMKG), with data time spanning from the 1970s to 2020. The analysis results show a significant difference between extreme rainfall analysis computed by using station data and the satellite. The use of the correction function can increase the accuracy of the GPM rainfall product. It can be used as an alternative data source for a region with limited rainfall stations.

Vertical structures of abrupt heavy rainfall events over southwest China with complex topography detected by dual‐frequency precipitation radar of global precipitation measurement satellite

AbstractSouthwest China is with complex topography including basins, mountains, hills and plains, where abrupt heavy rainfall events (AHREs) occur frequently and are difficult to quantitatively estimate due to limited ground‐based observations. Using the data of ground rain gauges and GPM dual‐frequency precipitation radar during April to September in 2014–2020, this study investigates vertical structures of AHREs over southwest China. Both mass‐weighted mean diameter (Dm) and reflectivity (Ze) of AHREs increase rapidly in ice‐phase process but slowly in liquid‐phase process. For convective rainfall of AHREs, abundant water vapour and strong atmospheric convective motion cause higher Dm and Ze but lower generalized intercept parameter (dBNw) than those for stratiform rainfall. Moreover, ice‐phase process is active while liquid‐phase process is weak in the mountains, but the situation is opposite in the plains, which results in large‐size and low‐concentration raindrops in the mountains while small‐size and high‐concentration raindrops in the plains. Furthermore, statistical models of vertical profile of reflectivity (VPR) indicate that VPR patterns are affected by surface rain intensity and present different trends around the 0°C level between stratiform and convective rainfalls. In addition, higher rain top height in the mountains is conducive to ice‐phase process while lower terrain height in the plains is favourable for liquid‐phase process. Therefore, the VPR pattern depends on rain type, terrain and rainfall intensity, and its fine model is beneficial for understanding microphysical process of AHREs and improving quantitative precipitation estimation by ground‐based radars.

An Identification Method of Melting Layer Using the Covariance Wavelet Transform Based on GPM‐DPR Observations

AbstractA knowledge of melting layer (ML) is crucial for studying precipitation and hydrometeor. Based on the measurements of dual‐frequency ratio (DFRm) from the dual‐frequency precipitation radar on board the Global Precipitation Measurement satellite, we use the covariance wavelet transform (CWT) method to identify the ML top height (MLTH) through the DFRm profiles. Due to the influence of disturbance, the directly retrieved MLTH could be estimated mistakenly. To improve the reliability of the retrieved MLTH, a quality‐control algorithm based on the CWT method is developed. By comparing the MLTH with the corresponding 0°C isothermal heights, the retrieved MLTH are more reliable than the dual‐frequency precipitation radar (DPR) product, showing great potential in deploying the Global Precipitation Measurement DPR and the CWT method in the identification of the MLTH.

Characteristics of Melting Layer in Cyclones Over the Western North Pacific Detected by the GPM Dual‐Frequency Precipitation Radar

AbstractA knowledge of melting layer (ML) is crucial for the accurate estimation of rainfall and the study of microphysical characterization of the cloud. Based on 5 years (2016–2020) of measurements of cyclones over the Western North Pacific from the dual‐frequency precipitation radar on board the Global Precipitation Measurement satellite, the characteristics and microphysical processes of ML are investigated. Our research shows that the height of the top boundary of ML is higher than the freezing level (0 degrees centigrade level, FL), and the Ze of freezing level is larger than that of the top of ML. The height of ML is the result of the combined effect of latitude and stage of cyclone. When the ice particles pass through the ML, the mass‐weighted mean diameter (Dm), the generalized intercept parameter (Nw), and the distributions of the reflectivity factor (Ze) increase, indicating that growth mechanism and breakup process.

Microphysics of Convective and Stratiform Precipitation during the Summer Monsoon Season over the Yangtze–Huaihe River Valley, China

Abstract Precipitation microphysics are critical for precipitation estimation and forecasting in numerical models. Using six years of observations from the Global Precipitation Measurement satellite, the spatial characteristics of precipitation microphysics are examined during the summer monsoon season over the Yangtze–Huaihe River valley. The results indicate that the heaviest convective rainfall is located mainly between the Huaihe and Yangtze Rivers, associated with a smaller mass-weighted mean diameter (Dm = ∼1.65 mm) and a larger mean generalized intercept parameter (Nw) (∼41 dBNw) at 2 km in altitude than those over the surrounding regions. Further, the convection in this region also has the lowest polarization-corrected temperature at 89 GHz (PCT89 < 254 K), indicating high concentrations of ice hydrometeors. For a given rainfall intensity, stratiform precipitation is characterized by a smaller mean Dm than convective precipitation. Below 4.5 km in altitude, the vertical slope of medium reflectivity factor varies with the rainfall intensity, which decreases slightly downward for light rain (<2.5 mm h−1), increases slightly for moderate rain (2.5–7.9 mm h−1), and increases more sharply for heavy rain (≥8 mm h−1) for both convective and stratiform precipitation. The increase in the amplitude of heavy rain for stratiform precipitation is much higher than that for convective precipitation, probably due to more efficient growth by warm rain processes. The PCT89 values have a greater potential to inform the near-surface microphysical parameters in convective precipitation compared with stratiform precipitation.

Passive Microwave Signatures and Retrieval of High-Latitude Snowfall Over Open Oceans and Sea Ice: Insights From Coincidences of GPM and CloudSat Satellites

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> database populated with the active–passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mmh <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> .