Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars

2019 Spring.Includes bibliographical references.Precipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has capability of providing a high-resolution vertical profile of precipitation over the tropics regions. Its successor, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This thesis presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train spaceborne radar data in order to get space based rainfall product. Therein, data alignment between spaceborne and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of spaceborne radar observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train both TRMM PR and GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the standard satellite products, which shows great potential of the machine learning concept in satellite radar rainfall estimation. Also, the local rain maps generated by machine learning system at KMLB area are demonstrate the application potential

Cross-validation of active and passive microwave snowfall products over the continental United States

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’sCoreObservatorysensors and theCloudSatradar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radarcomposite product over the continental United States during the period from November 2014 to September 2020. Theanalysis includes the Dual-Frequency Precipitation Radar (DPR) retrieval and its single-frequency counterparts, the GPMCombined Radar Radiometer Algorithm (CORRA), theCloudSatSnow Profile product (2C-SNOW-PROFILE), and twopassive microwave retrievals, i.e., the Goddard Profiling algorithm (GPROF) and the Snow Retrieval Algorithm for GMI(SLALOM). The 2C-SNOW retrieval has the highest Heidke skill score (HSS) for detecting snowfall among the productsanalyzed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of thesnow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in theGMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall ratesby a factor of 2 compared to MRMS. Large discrepancies (RMSE of 0.7–1.5 mm h21) between spaceborne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of theremote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by theconfounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers

Global Precipitation Measurement (GPM): Unified Precipitation Estimation From Space

Global Precipitation Measurement (GPM) is an international satellite mission that uses measurements from an advanced radar/radiometer system on a Core Observatory as reference standards to unify and advance precipitation estimates through a constellation of research and operational microwave sensors. GPM is a science mission focusing on a key component of the Earth's water and energy cycle, delivering near real-time observations of precipitation for monitoring severe weather events, freshwater resources, and other societal applications. This work presents the GPM mission design, together with descriptions of sensor characteristics, inter-satellite calibration, retrieval methodologies, ground validation activities, and societal applications

The GPM GV Program

We present a detailed overview of the structure and activities associated with the NASA-led ground validation component of the NASA-JAXA Global Precipitation Measurement (GPM) mission. The overarching philosophy and approaches for NASAs GV program are presented with primary focus placed on aspects of direct validation and a summary of physical validation campaigns and results. We describe a spectrum of key instruments, methods, field campaigns and data products developed and used by NASAs GV team to verify GPM level-2 precipitation products in rain and snow. We describe the tools and analysis framework used to confirm that NASAs Level-1 science requirements for GPM are met by the GPM Core Observatory. Examples of routine validation activities related to verification of Integrated Multi-satellitE Retrievals for GPM (IMERG) products for two different regions of the globe (Korea and the U.S.) are provided, and a brief analysis related to IMERG performance in the extreme rainfall event associated with Hurricane Florence is discussed

Global Precipitation Measurement

This chapter begins with a brief history and background of microwave precipitation sensors, with a discussion of the sensitivity of both passive and active instruments, to trace the evolution of satellite-based rainfall techniques from an era of inference to an era of physical measurement. Next, the highly successful Tropical Rainfall Measuring Mission will be described, followed by the goals and plans for the Global Precipitation Measurement (GPM) Mission and the status of precipitation retrieval algorithm development. The chapter concludes with a summary of the need for space-based precipitation measurement, current technological capabilities, near-term algorithm advancements and anticipated new sciences and societal benefits in the GPM era

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

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

NASA's Global Precipitation Mission Ground Validation Segment

NASA is designing a Ground Validation Segment (GVS) as one of its contributions to the Global Precipitation Measurement (GPM) mission. The GPM GVS provides an independent means for evaluation, diagnosis, and ultimately improvement of the GPM spaceborne measurements and precipitation products. NASA's GPM GVS concept calls for a combination of direct observations executed within a Multidimensional Observing Volume (MOV) and model-based analyses executed by a Satellite Simulator Model (SSM). The MOV consists of ground-based instruments that measure local surface and atmospheric properties required for GPM validation. The SSM utilizes MOV measurements in a forward numerical model. The goal of the SSM forward modeling is calculation of the following properties: top-of-atmosphere microwave radiative quantities to within sensor noise of those measured by the GPM Core Satellite, precipitation quantities identical to those generated by the standard GPM precipitation retrieval algorithms, and quantitative/objective error estimates of both sets of quantities. At present, the GVS is in the early design stage and various scenarios have been generated to assess how it will be used in the GPM era. The GPM GVS will be operational in the year prior to the launch of the GPM core satellite, which has a launch date scheduled for December 2010

Improving active remote sensing retrievals of snowfall at microwave wavelengths: an emphasis on the global precipitation measurement mission’s dual-frequency precipitation radar

Even though snowfall at the surface is often constrained to higher latitudes or altitudes, the contribution of solid-phase hydrometeors to the hydrologic cycle is not trivial and can be related to more than 50% of all surface rain events. Furthermore, the quantification of snow and ice in the atmospheric column is required to understand the Earth’s outgoing thermal radiative budget. Thus, the retrieval of snowfall from spaceborne radars that can sample remote regions of the world is invaluable for both atmospheric and climate sciences. One spaceborne radar capable of measuring snowfall is the Global Precipitation Measurement mission’s Dual-frequency Precipitation Radar (GPM-DPR). Initial evaluations of the retrieval of near-surface snowfall from GPM-DPR against the common global snowfall reference (i.e., CloudSat) showed large discrepancies between the two radar retrieval estimates. The large discrepancy between the CloudSat and GPM-DPR snowfall retrieval served as the main motivation for the work conducted here. Three tasks were formulated and conducted in this dissertation: (1) Evaluate the assumptions within the current GPM-DPR retrieval of snowfall; (2) Create an alternative retrieval for GPM-DPR; (3) Compare the new retrieval to the old retrieval methods. Task 1 is found in Chapter 2, Task 2 is in Chapter 3 and Task 3 is in Chapter 4. For Task 1, the investigation of ground-based measurements of both rain and snow and their particle size distributions allowed for the assessment of the main microphysical assumption of the GPM-DPR retrieval, which assumes that all hydrometeors obey the same empirical relationship between the precipitation rate (R) and the mass-weighted mean diameter (D_m). Rainfall observations showed that the default R-D_m relation for rainfall is plausible and shows general consistency with a Pearson ρ correlation coefficient of 0.63. However, snowfall observations showed that the R-D_m relation does not apply well for snowfall resulting in the underestimation of R. Furthermore, the low correlation between the log⁡〖(R〗) and D_m (ρ=0.23) suggests that an R-D_m retrieval is not optimal for snowfall retrievals and other methods should be explored. Motivated from the results of Task 1, an alternative retrieval for GPM-DPR was designed in Task 2 using a neural network, state-of-the-art particle scattering models and measured particle size distributions. The main result from Task 2 is that the neural network retrieval significantly improves (p<0.05) the mean squared error of the retrieval of ice water content (IWC) compared to old power-law methods and an estimate of the current GPM-DPR algorithm. This was shown in the evaluation of the retrieval on a subset of synthetic data that was not used in training the neural network as well as in three case studies from NASA field campaigns where independent observations of radar reflectivity and in-situ parameters were made. Finally, Task 3 evaluated the newly formulated retrieval from Task 2 against the operational CloudSat product (2C-SNOWPROFILE) and the current GPM-DPR algorithm. The evaluation is done using a premade coincident dataset of both CloudSat and GPM-DPR which allowed for the direct comparison of all retrieval methods. Comparing the three retrievals show that on average the neural network retrieval performs best, predicting R just below the melting layer to within 2%. A secondary result from Task 3 is that the 2C-SNOWPROFLE retrieval is likely underestimating R for moderate to intense snowfall events signified by a 35% reduction of R from -15°C to the melting layer

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