衛星搭載レーダにより明らかとなったアラスカ南岸における大きな降水勾配

This study focuses on the considerable spatial variability of precipitation along the western coast of a continent at mid-high latitude and investigates the precipitation climatology and mechanism along the south coast of Alaska, using datasets of spaceborne radars onboard two satellites, namely, the Dual-frequency Precipitation Radar (DPR) KuPR onboard the Global Precipitation Measurement (GPM) core satellite and the Cloud Profiling Radar (CPR) onboard CloudSat. At higher latitudes, differentiating the phase of precipitation particles falling on the ground is crucial in evaluating precipitation. Classification of satellite precipitation products according to the distance from the coastline shows that precipitation characteristics differ greatly on opposite sides of the coastline. Above coastal waters, relatively heavy precipitation with CPR reflectivity larger than 7 dBZ from orographically enhanced nimbostratus clouds, which can be detected by KuPR, is frequently captured. Meanwhile, along coastal mountains, light-to-moderate snowfall events with CPR reflectivity lower than 11 dBZ, which are well detected by the CPR but rarely detected by KuPR, frequently occur, and they are mainly brought by nimbostratus clouds advected from the coast and orographically enhanced shallow cumuliform clouds. There is no clear diurnal variation of precipitation except in summer, and the amplitude of the variation during summer is still low compared with total precipitation especially over the ocean, suggesting that the transport of synoptic-scale water vapor brings much precipitation throughout the year. Case studies and seasonal analysis indicate that frontal systems and moisture flows associated with extratropical cyclones that arrive from the Gulf of Alaska are blocked by terrain and stagnate along the coast to yield long-lasting precipitation along the coastline. The results of this study illustrate the importance of using complementary information provided by these radars to evaluate the precipitation climatology in a region in which both rainfall and snowfall occur.本研究は、空間変動の大きい中高緯度大陸西岸の降水に焦点を当て、全球降水観測計画（GPM）主衛星搭載二周波降水レーダ（DPR）Ku帯降水レーダ（KuPR）およびCloudSat衛星搭載雲レーダ（CPR）を用いてアラスカ南岸の気候学的な降水分布や降水メカニズムについて調査した。高緯度では地表へ落下する降水粒子の相を判別することが降水を評価するうえで不可欠である。海岸線からの距離によって衛星降水プロダクトを分類することで、海岸線を挟んだ海側と陸側で降水特性が大きく異なっていることを示した。沿岸の海上では、地形効果で強化された乱層雲からのCPR反射強度７dBZ以上の比較的強い降水が頻繁にとらえられており、KuPRでもとらえられている。一方、海岸山脈上では、CPR反射強度11dBZ以下の弱～中程度の降雪が頻繁に発生していることが、CPRでとらえられているがKuPRではほとんどとらえられていない。この雪は主に海岸域より移流してきた乱層雲や地形効果を受けて強まった浅い対流雲によってもたらされている。夏季を除いて顕著な降水の日周期変動はなく、さらに夏季の日周期変動の振幅も総降水量と比べると特に海上で小さく、総観規模の水蒸気輸送が年間を通して多くの降水をもたらしていることを示唆している。事例解析と季節解析により、アラスカ湾から到来する温帯低気圧に伴う前線システム及び水蒸気の流れが、海岸沿いで地形によりブロックされて停滞し、沿岸に長く持続した降水をもたらしていることが示された。本研究の結果は、降雨・降雪の両方が発生する地域の降水気候値を評価するには、これら２つのレーダの相補的な情報を用いることが重要であることを示している

GPM-DPR Observations on TGFs Producing Storms

Unique spaceborne measurements of the three-dimensional structure of convective clouds producing terrestrial gamma ray flashes (TGFs) were performed using both active and passive microwave sensors on board the Global Precipitation Measurement (GPM)-Core Observatory satellite, finding coherent features for nine TGF-producing storms. The delineation of cloud structure using the radar reflectivity factor shows convective cells with significant vertical development and thick layers with high ice content. Compared to other cumulonimbus clouds in the tropics, the TGFs counterparts have higher reflectivity values above 3 and 8 km altitude showing in all cases a cumulonimbus tower and the TGFs locations are very close, or coincident, to these high Z columns, where reflectivity exceeds 50dBz. Using the GPM Microwave Imager radiometer, most thunderstorms show a very strong depression of polarization corrected temperature (PCT) at channel 89GHz, indicating a strong scattering signal by ice in the upper cloud layers. At channel 166GHZ, the difference between vertical and horizontal brightness temperature signal always returns positive values, from 0.2 up to 13.7K indicating a complex structure with randomly/vertically oriented ice particles. The PCT was used to characterize the analyzed storms in terms of hydrometeor types, confirming in 7/9 cases a high likelihood of hail/graupel presence. To perform analysis on the TGFs parent flashes, radio atmospherics data from the Earth Networks Total Lightning Network lightning network were used. Waveform data indicate that all cases are intra-cloud events and TGFs typically take place during the peak of flash rate production. Finally, the analysis of the most intense event is shown

SLALOM: An all-surface snow water path retrieval algorithm for the GPM microwave imager

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI

Validating Precipitation Phase Measurements From Dual-Frequency Precipitation Radar On GPM Core Observatory Satellite

The purpose of this project is to validate precipitation measurements from the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO) satellite. The GPM-CO satellite is being used to detect falling rain and snow. Being able to detect rain builds off the success of the Tropical Rainfall Measuring Mission (TRMM), which provided reasonable rainfall estimates when compared to ground-based radars. Detecting falling snow was a key GPM-CO requirement that was to be met within three years the satellite’s launch date of 27 February 2014. In this project, ground observations from Automated Surface Observing System (ASOS) and Automated Weather Observing Station (AWOS) was used to determine how well GPM-CO’s Dual-frequency Precipitation Radar (DPR) can detect and classify precipitation phase. If GPM can detect precipitation, especially snow, it could lead to increased knowledge of fresh water resources. GPM can lead to a better understanding of the full picture of the water cycle and the effects precipitation has on the availability of fresh water. This can result in identifying patterns of precipitation systems over land. Results show that DPR struggles to detect solid precipitation (snow), but if detected, then DPR successfully determines the phase. DPR detects liquid precipitation better than solid precipitation but does not do as well at classifying it. Results also show that performance is not as good over complex terrain. These are promising results as they show that GPM-CO satellite meets its requirement of detecting falling snow. Other results show that it is successful at detecting and classifying rainfall as well

Assessing Precipitation Delineation Capabilities of Spaceborne Radars

Spaceborne radars uniquely measure, provide the finest depiction of, and give the most accurate estimate of precipitation globally from space. The Global Precipitation Measurement (GPM) mission dual-frequency precipitation radar (DPR) is the successor to the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), expanding on its capabilities with a dual-frequency radar and coverage into the midlatitudes. The consistent ability to detect various precipitation magnitudes across satellite missions is critical to the study of global precipitation over various time periods. The precipitation delineation capabilities of spaceborne radars are characterized as functions of their reflectivity and the corresponding precipitation magnitude from the reference Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) over CONUS. The Heidke Skill Score, a measure of skill with respect to random chance, is computed to synthesize the capabilities of the spaceborne radars. This enables a finer depiction and interpretation of spaceborne radar capabilities than the bulk metrics widely used in the literature. Skill is more sensitive to changes in rain rate at lower rain rates and changes in reflectivity at higher rain rates. The TRMM-PR and GPM-DPR best delineate moderate precipitation while the GPM-KuPR detects precipitation with low to moderate skill. While both the TRMM-PR and GPM-DPR perform better than GPM-KuPR, the TRMM-PR performs better at lower reflectivity thresholds while the GPM-DPR performs better at higher reflectivity thresholds. Certain factors do not have a significant impact on the overall skill. Others have a significant impact, but the number of cases were small and did not greatly impact the overall skill. While the GPM-DPR struggles with the detection of precipitation, and the TRMM-PR performs the best overall, both the GPM-DPR and TRMM-PR have good delineation capabilities

Improving the quality of extreme precipitation estimates using satellite passive microwave rainfall retrievals

2017 Summer.Includes bibliographical references.Satellite rainfall estimates are invaluable in assessing global precipitation. As a part of the Global Precipitation Measurement (GPM) mission, a constellation of orbiting sensors, dominated by passive microwave imagers, provides a full coverage of the planet approximately every 2-3 hours. Several decades of development have resulted in passive microwave rainfall retrievals that are indispensable in addressing global precipitation climatology. However, this prominent achievement is often overshadowed by the retrieval's performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate rainfall measurements. This is especially true over land, where rainfall estimates are based on an observed mean relationship between high frequency (e.g., 89 GHz) brightness temperature (Tb) depression (i.e., the ice-scattering signature) and rainfall rate. In the first part of this study, an extreme precipitation event that caused historical flooding over south-east Europe is analyzed using the GPM constellation. Performance of the rainfall retrieval is evaluated against ground radar and gage reference. It is concluded that satellite observations fully address the temporal evolution of the event but greatly underestimate total rainfall accumulation (by factor of 2.5). A primary limitation of the rainfall algorithm is found to be its inability to recognize variability in precipitating system structure. This variability is closely related to the structure of the precipitation regime and the large-scale environment. To address this influence of rainfall physics on the overall retrieval bias, the second part of this study utilizes TRMM radar (PR) and radiometer (TMI) observations to first confirm that the Tb-to-rain-rate relationship is governed by the amount of ice in the atmospheric column. Then, using the Amazon and Central African regions as testbeds, it demonstrates that the amount of ice aloft is strongly linked to a precipitation regime. A correlation found between the large-scale environment and precipitation regimes is then further examined. Variables such as Convective Available Potential Energy (CAPE), Cloud Condensation Nuclei (CCN), wind shear, and vertical humidity profiles are found to be capable of predicting a precipitation regime and explaining up to 40% of climatological biases. Dry over moist air conditions are favorable for developing intense, well organized systems such as MCSs in West Africa and the Sahel. These systems are characterized by strong Tb depressions and above average amounts of ice aloft. As a consequence, microwave retrieval algorithms misinterpret these non-typical systems assigning them unrealistically high rainfall rates. The opposite is true in the Amazon region, where observed raining systems exhibit relatively little ice while producing high rainfall rates. Based on these findings, in the last part of the study, the GPM operational retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. When forming an estimate, the modified algorithm is allowed to use this ancillary information to filter out a priori states that do not match the general environmental condition relevant to the observation and thus reduce the difference between the assumed and observed variability in ice-to-rain ratio. The results are compared to the ground Multi-Radar Multi-Sensor (MRMS) network over the US at various spatial and temporal scales demonstrating outstanding potentials in improving the accuracy of rainfall estimates from satellite-borne passive microwave sensors over land

Inter-comparison Of Reflectivity Measurements Between GPM DPR And NEXRAD Radars

This study demonstrates the potential use of the NASA\u27s Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) to examine ground radar (GR) miscalibration and other uncertainty sources (e.g., partial beam blockage). We acquired the GPM Ground Validation System Validation Network reflectivity matchups between the DPR and three GRs (two in Iowa and one in South Dakota) for 2014–2017. We then refined the matching parameters (e.g., time separation) to reduce uncertainty in the matchup samples by analyzing the sensitivity of the matchup statistical properties to changes in these parameters. To reconcile the same observables (i.e., reflectivity) with different observational properties among the space- and ground-based radars, we developed a statistically integrated framework using inter-comparisons of them all with a Monte Carlo simulation. This method verifies the absolute calibration bias estimated from the refined DPR–GR matchups using relative calibration biases between GRs. We found that taking samples with a narrow temporal gap, estimated by actual measurement time of the DPR and GRs, can significantly reduce sample variability. Through inter-comparisons among the DPR and GRs, we observed that reflectivity differences among GRs in a similar environment (e.g., climatology and geography) are likely to be affected primarily by the calibration mismatch. In this case, the inter-comparison results demonstrated good agreement, and we inferred that the differences can be mitigated by calibration bias correction against the DPR. On the other hand, when the disagreement level of the inter-comparison results is significant, the authors found that other factors, such as partial beam blockage even in relatively plain regions, are more dominant than the calibration bias. In fact, the partial beam blockage effects can manifest themselves as a seasonal pattern in the GR inter-comparison results

Evaluation of Precipitation Estimates by at-Launch Codes of GPM/DPR Algorithms Using Synthetic Data from TRMM/PR Observations

The Global Precipitation Measurement (GPM) Core Observatory will carry a Dual-frequency Precipitation Radar (DPR) consisting of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). In this study, \u27at-launch\u27 codes of DPR precipitation algorithms, which will be used in GPM ground systems at launch, were evaluated using synthetic data based upon the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. Results from the codes (Version 4.20131010) of the KuPR-only, KaPR-only, and DPR algorithms were compared with \u27true values\u27 calculated based upon drop size distributions assumed in the synthetic data and standard results from the TRMM algorithms at an altitude of 2 km over the ocean. The results indicate that the total precipitation amounts during April 2011 from the KuPR and DPR algorithms are similar to the true values, whereas the estimates from the KaPR data are underestimated. Moreover, the DPR estimates yielded smaller precipitation rates for rates less than about 10 mm/h and greater precipitation rates above 10 mm/h. Underestimation of the KaPR estimates was analyzed in terms of measured radar reflectivity ({\bf Z}-{\bf m}) of the KaPR at an altitude of 2 km. The underestimation of the KaPR data was most pronounced during strong precipitation events of {\bf Z}-{\bf m} \lt {\bf 18}~{\bf dBZ} (high attenuation cases) over heavy precipitation areas in the Tropics, whereas the underestimation was less pronounced when the {\bf Z}-{\bf m}\gt 26~{\bf dBZ} (moderate attenuation cases). The results suggest that the underestimation is caused by a problem in the attenuation correction method, which was verified by the improved codes

A Dual-Wavelength Radar Technique to Detect Hydrometeor Phases

This study is aimed at investigating the feasibility of a Ku- and Ka-band space/air-borne dual wavelength radar algorithm to discriminate various phase states of precipitating hydrometeors. A phase-state classification algorithm has been developed from the radar measurements of snow, mixed-phase and rain obtained from stratiform storms. The algorithm, presented in the form of the look-up table that links the Ku-band radar reflectivities and dual-frequency ratio (DFR) to the phase states of hydrometeors, is checked by applying it to the measurements of the Jet Propulsion Laboratory, California Institute of Technology, Airborne Precipitation Radar Second Generation (APR-2). In creating the statistically-based phase look-up table, the attenuation corrected (or true) radar reflectivity factors are employed, leading to better accuracy in determining the hydrometeor phase. In practice, however, the true radar reflectivities are not always available before the phase states of the hydrometeors are determined. Therefore, it is desirable to make use of the measured radar reflectivities in classifying the phase states. To do this, a phase-identification procedure is proposed that uses only measured radar reflectivities. The procedure is then tested using APR-2 airborne radar data. Analysis of the classification results in stratiform rain indicates that the regions of snow, mixed-phase and rain derived from the phase-identification algorithm coincide reasonably well with those determined from the measured radar reflectivities and linear depolarization ratio (LDR)