scholarly journals Ground Validation of Surface Snowfall Algorithm in GPM Dual-Frequency Precipitation Radar

2019 ◽  
Vol 36 (4) ◽  
pp. 607-619 ◽  
Author(s):  
Minda Le ◽  
V. Chandrasekar
Keyword(s):  
Ground Truth ◽  
Identification Algorithm ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Ground Validation ◽  
Potential Improvement ◽  
Global Precipitation Mission ◽  
Global Precipitation ◽  
Level 2 ◽  
Precipitation Events

AbstractExtensive evaluations have been performed on the dual-frequency classification module in the Global Precipitation Mission (GPM) Dual-Frequency Precipitation Radar (DPR) level-2 algorithm. Both rain type classification and melting-layer detection continue to show promising results in the validations. Surface snowfall identification is a feature newly added in the classification module to the recently released version to provide a surface snowfall flag for each qualified vertical profile. This algorithm is developed upon vertical features of Ku- and Ka-band reflectivity and dual-frequency ratio from DPR. In this paper, we validate this surface snowfall identification algorithm with ground radars including NEXRAD, NASA Polarimetric Radar (NPOL), and CSU–CHILL radar during concurrent precipitation events and GPM validation campaign Olympic Mountain Experiment (OLYMPEX). Other ground truth such as Precipitation Imaging Package (PIP) and ground report is also included in the validation. Based on 16 validation cases in the years 2014–18, the average match ratio between surface snowfall flag from space radar and ground radar is around 87.8%. Promising agreements are achieved with different validation sources. Algorithm limitation and potential improvement are discussed.

scholarly journals Retrieving Rain Drop Size Distribution Moments from GPM Dual-Frequency Precipitation Radar

2021 ◽  
Vol 13 (22) ◽  
pp. 4690
Author(s):  
Merhala Thurai ◽  
Viswanathan Bringi ◽  
David Wolff ◽  
David A. Marks ◽  
Patrick N. Gatlin ◽  
...  
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Drop Size Distribution ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Distribution Moments ◽  
The Us ◽  
Rain Drop ◽  
Global Precipitation Mission ◽  
Global Precipitation

A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, namely the attenuation-corrected Ku-band radar reflectivity and (if made available) the specific attenuation at Ka-band. The reference moments are then combined with a function representing the underlying shape of the DSD based on the generalized gamma model. Simulations are performed to quantify the algorithm errors. The performance of methodology is assessed with two GPM-DPR overpass cases over disdrometer sites, one in Huntsville, Alabama and one in Delmarva peninsula, Virginia, both in the US. Results are promising and indicate that it is feasible to estimate DSD moments directly from DPR-based quantities.

scholarly journals Sensitivity of 89–190-GHz Microwave Observations to Ice Particle Scattering

2020 ◽  
Vol 59 (7) ◽  
pp. 1195-1215
Author(s):  
Ruiyao Chen ◽  
Ralf Bennartz
Keyword(s):  
Radar Reflectivity ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Humidity Sounder ◽  
Slant Path ◽  
The One ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractThe sensitivity of microwave brightness temperatures (TBs) to hydrometeors at frequencies between 89 and 190 GHz is investigated by comparing Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) measurements with radar reflectivity profiles and retrieved products from the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (DPR). Scattering-induced TB depressions (ΔTBs), calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel, are compared with DPR-retrieved hydrometeor water path (HWP) and vertically integrated radar reflectivity ZINT. We also account for the number of hydrometeors actually visible in each MWHS-2 channel by weighting HWP with the channel’s cloud-free gas transmission profile and the observation slant path. We denote these transmission-weighted, slant-path-integrated quantities with a superscript asterisk (e.g., HWP*). The so-derived linear sensitivity of ΔTB with respect to HWP* increases with frequency roughly to the power of 1.78. A retrieved HWP* of 1 kg m−2 at 89 GHz on average corresponds to a decrease in observed TB, relative to a cloud-free background, of 11 K. At 183 GHz, the decrease is about 34–53 K. We perform a similar analysis using the vertically integrated, transmission-weighted slant-path radar reflectivity and find that ΔTB also decreases approximately linearly with . The exponent of 0.58 corresponds to the one we find in the purely DPR-retrieval-based ZINT–HWP relation. The observed sensitivities of ΔTB with respect to and HWP* allow for the validation of hydrometeor scattering models.

GPM DPR Profile Classification Algorithm: Enhancement from V6X to V7

2021 ◽  
Author(s):  
Chandrasekar V Chandra ◽  
Minda Le
Keyword(s):  
Vertical Profile ◽  
Smooth Transition ◽  
Identification Algorithm ◽  
Dual Frequency ◽  
A Value ◽  
Extensive Evaluation ◽  
Precipitation Type ◽  
Boolean Product ◽  
Level 2 ◽  
Type Classification

<p>The profile classification module in GPM DPR level-2 algorithm outputs various products  such as rain type classification, melting layer  detection and  identification of  surface snowfall , as well as presence of graupel and hail. Extensive evaluation and validation activities have been performed on these products and have illustrated excellent performance. The latest version of these products is 6X.  With increasing interests  on severe weather  such as hail and  extreme precipitation, in  the next version (version 7), we development a flag to identify hail along the vertical profile using  precipitation type index (PTI).</p><p>Precipitation type index (PTI) plays an important role in a couple of algorithms in the profile classification module. PTI is a value calculated for each dual-frequency profile with precipitation observed by GPM DPR.   DFRm slope, the maximum value of the Zm(Ku) , and  storm top height  are used in calculating PTI. PTI is effective in separating snow and Graupel/Hail  profiles. In version 7, we zoom in further into PTI for  Graupel/ hail profiles and separate  them into graupel and hail profiles with different PTI thresholds. A new Boolean product of “flagHail” is a hail only identifier for each vertical profile.  This hail product will be validated with ground radar products and other DPR products from Trigger module of DPR level-2 algorithm.   In version 7, we make improvements of the surface snowfall algorithm. An adjustment is made accounting for global variability of storm top profiles.. A storm top normalization is introduced to obtain a smooth transition of surface snowfall identification algorithm along varying latitudes globally.</p>

Validation of satellite-borne precipitation radars by raingauges and disdrometers over the northeastern Indian subcontinent

2021 ◽  
Author(s):  
Fumie Murata ◽  
Toru Terao ◽  
Yusuke Yamane ◽  
Masashi Kiguchi ◽  
Azusa Fukushima ◽  
...  
Keyword(s):  
Indian Subcontinent ◽  
Monsoon Season ◽  
Precipitation Radar ◽  
Near Surface ◽  
Trmm Pr ◽  
Rain Drop ◽  
Distribution Parameters ◽  
Trmm Precipitation ◽  
Global Precipitation Mission ◽  
Global Precipitation

<p>The near surface rain (NSR) dataset of the Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) and the Global Precipitation Mission (GPM) Dual Precipitation Radar (DPR) was validated using around 40 tipping bucket raingauges installed over the northeastern Indian subcontinent, and disdrometers in the Meghalaya Plateau, India. The comparison during 2006-2014 showed significant overestimation of TRMM PR in Assam and Bengal plains during pre-monsoon season (March to May), and significant underestimation of TRMM PR over the Indian subcontinent during monsoon season (June to September). Whereas, the comparison during 2014-2019 showed significant overestimation of GPM DPR over only Meghalaya during monsoon season. The validation of rain-drop size distribution parameters: Dm and Nw showed positive correlation between GPM DPR derived values and Parsivel disdrometers observed ones, while unrealistic concentration of Nw on 30-40 dB was derived by GPM DPR. In the southern slope of the Meghala Plateau, NSR of TRMM PR at Cherrapunji, where is known as the heaviest rainfall station, on the plateau observed smaller rainfall than that in the adjacent valley. However, newly installed raingauges in the valley showed rather less rainfall than that on the plateau. The validity of the satellite derived rainfall distribution over the complicated terrain are discussed.</p>

Understanding performance of GPROF precipitation retrievals over the Netherlands in relation to precipitation characteristics as derived from ground-based dual-polarization radars

2021 ◽  
Author(s):  
Linda Bogerd ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
Remko Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Background Radiation ◽  
A Priori ◽  
Dual Frequency ◽  
Dual Polarization ◽  
Ground Clutter ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Precipitation Events

<p>The Global Precipitation Measurement mission (GPM) is one of the recent efforts to provide satellite-based global precipitation estimates. The GPM Profiling Algorithm (GPROF) converts microwave radiation measured by passive microwave (PMW) sensors onboard constellation satellites into precipitation. Over land, precipitation estimates are obtained from high frequency PMW-channels that measure the radiance scattered by ice particles in rain clouds. However, due to the limited scattering related to shallow and light precipitation, it is challenging to distinguish these signals from background radiation that is naturally emitted from the Earth’s surface.</p><p>Increased understanding of the physical processes during precipitation events can be used to improve PMW-based precipitation retrievals. This study couples overpasses of GPM radiometers over the Netherlands to two dual-polarization radars from the Royal Netherlands Meteorological Institute (KNMI). The Netherlands is an ideal setting for this study due to the availability of high-quality ground-based measurements, the frequent occurrence of shallow events, the absence of ground-clutter related to mountains, and the varying background emission related to its coastal location.</p><p>The coupling of overpasses with ground-based precipitation radars provides the opportunity to relate GPROFs performance to physical characteristics of precipitation events, such as the vertical reflectivity profile and dual-polarization information on the melting layer. Furthermore, simultaneous radiometer estimates and space-based reflectivity profiles from the dual-frequency precipitation radar (DPR) onboard the GPM core satellite are coupled to the ground-based reflectivity profiles for selected case studies. Because the a-priori database implemented in the GPROF algorithm is based on observations from the DPR, the comparison of the reflectivity profiles further unravels discrepancies between GPROF and ground-based estimates.</p>

scholarly journals Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective

2019 ◽  
Vol 58 (7) ◽  
pp. 1429-1448 ◽  
Author(s):  
Gail Skofronick-Jackson ◽  
Mark Kulie ◽  
Lisa Milani ◽  
Stephen J. Munchak ◽  
Norman B. Wood ◽  
...  
Keyword(s):  
Particle Size ◽  
Size Distributions ◽  
Classification Methods ◽  
Dual Frequency ◽  
Particle Size Distributions ◽  
Precipitation Radar ◽  
W Band ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractRetrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.

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