TOWARDS IMPROVED QPE BY CAPITALIZING GROUND- AND SPACE- BASED PRECIPITATION MEASUREMENTS

There are primarily two types of weather radar systems offering precipitation measurements covering relatively large areas: (1) Ground-based Radar (GR) networks such as the NEXRAD and (2) Spaceborne radars onboard meteorological satellites. Ground-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of space-borne weather radar algorithms and observations. To achieve the synergy between ground- and space-borne weather radar, this study first aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory (NSSL) “proof of concept” polarimetric WSR-88D radar (KOUN) and the space-borne precipitation radar (PR) onboard NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. The comparisons reveal an overall bias <0.2% between PR and KOUN. The bias is hypothesized to be from non-Raleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. Provided the upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nation-wide validation of space precipitation products and also invite synergistic development of coordinated space/ground multisensor precipitation products. On the other hand, due to inadequate radar coverage from intervening terrain blockages, ground QPE needs enhancement aided by spaceborne radars. In the second part of the talk, I will introduce an approach that identifies and corrects for vertical profile of reflectivity (VPR) by using TRMM PR measurements in the region of Arizona and southern California, where ground-based NEXRAD radars are difficult to obtain reliable ground precipitation estimation due to complex terrain and limited radar coverage. A VPR Identification and Enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically-based parameterization and climatological information is employed to obtain VPRs at S-band from the TRMM PR measurement at Ku-band. The VPR-IE methodology is comprehensively evaluated with all stratiform precipitation events in cold season in the year of 2011. The results show that the VPR-IE has overall good performance and provides much more accurate surface rainfall estimates than original radar QPE in NMQ system. With the recent availability of GPM Dual-frequency PR, the VPR-IE approach is anticipated to be more robust and more useful by extending to higher latitude mountainous regions

Bayesian Correction Approach for Improving Dual-Frequency Precipitation Radar Rainfall Rate Estimates, A

Includes bibliographical references (pages 14-15).The accurate estimation of precipitation is an important objective for the Dual-frequency Precipitation Radar (DPR), which is located on board the Global Precipitation Measurement (GPM) satellite core observatory. In this study, a Bayesian correction (BC) approach is proposed to improve the DPR’s instantaneous rainfall rate product. Ground dual-polarization radar (GR) observations are used as references, and a log-transformed Gaussian distribution is assumed as the instantaneous rainfall process. Additionally, a generalized regression model is adopted in the BC algorithm. Rainfall intensities such as light, moderate, and heavy rain and their variable influences on the model’s performance are considered. The BC approach quantifies the predictive uncertainties associated with the Bayesiancorrected DPR (DPR_BC) rainfall rate estimates. To demonstrate the concepts developed in this study, data from the GPM overpasses of the Weather Service Surveillance Radar (WSR-88D), KHGX, in Houston, Texas, between April 2014 and June 2018 are used. Observation errors in the DPR instantaneous rainfall rate estimates are analyzed as a function of rainfall intensity. Moreover, the best-performing BC model is implemented in three GPM-overpass cases with heavy rainfall records across the southeastern United States. The results show that the DPR_BC rainfall rate estimates have superior skill scores and are in better agreement with the GR references than with the DPR estimates. This study demonstrates the potential of the proposed BC algorithm for enhancing the instantaneous rainfall rate product from spaceborne radar equipment

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 the Impact of Light/Shallow Precipitation Retrievals from Satellite-Based Observations Using Surface Radar and Micro Rain Radar Observations

The accurate representation of precipitation across the Earth’s surface is crucial to furthering our knowledge and understanding of the Earth System and its component processes. Precipitation poses a number of challenges, particularly due to the variability of precipitation over time and space and whether it falls as snow or rain. While conventional measures of precipitation are reasonably good at the location of their measurement, their distribution across the Earth’s surface is uneven with some regions having no surface measurements. Spaceborne sensors have the capability of providing regular observations across the Earth’s surface that can provide estimates of precipitation. However,the estimation of precipitation from satellite observations is not necessarily straightforward. Visible and/or infrared techniques rely upon imprecise cloud-top to surface precipitation relationships, while the sensitivity of passive microwave techniques to different precipitation types is not consistent. Active microwave (radar) observations provide the most direct satellite measurements of precipitation but cannot provide estimates close to the surface and are generally not sufficiently sensitive to resolve light precipitation. This is particularly problematic at mid to high latitudes, where light and/or shallow precipitation dominates. This paper compares measurements made by ground-based weather radars, Micro Rain Radars and the spaceborne Dual-frequency Precipitation Radar to study both light precipitation intensity and shallow precipitation occurrence and to assess their impact on satellites retrievals of precipitation at the mid to high latitudes

Comparison of the GPM IMERG Final Precipitation Product to RADOLAN Weather Radar Data over the Topographically and Climatically Diverse Germany

Precipitation measurements provide crucial information for hydrometeorological applications. In regions where typical precipitation measurement gauges are sparse, gridded products aim to provide alternative data sources. This study examines the performance of NASA's Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG, GPM) satellite precipitation dataset in capturing the spatio-temporal variability of weather events compared to the German weather radar dataset RADOLAN RW. Besides quantity, also timing of rainfall is of very high importance when modeling or monitoring the hydrologic cycle. Therefore, detection metrics are evaluated along with standard statistical measures to test both datasets. Using indices like probability of detection allows a binary evaluation showing the basic categorical accordance of the radar and satellite data. Furthermore, a pixel-by-pixel comparison is performed to assess the ability to represent the spatial variability of rainfall and precipitation quantity. All calculations are additionally carried out for seasonal subsets of the data to assess potentially different behavior due to differences in precipitation schemes. The results indicate significant differences between the datasets. Overall, GPM IMERG overestimates the quantity of precipitation compared to RADOLAN, especially in the winter season. Moreover, shortcomings in detection performance arise in this season with significant erroneously-detected, yet also missed precipitation events compared to the weather radar data. Additionally, along secondary mountain ranges and the Alps, topographically-induced precipitation is not represented in GPM data, which generally shows a lack of spatial variability in rainfall and snowfall estimates due to lower resolution

A polarimetric radar operator to evaluate precipitation from the COSMO atmospheric model

Weather radars provide real-time measurements of precipitation at a high temporal and spatial resolution and over a large domain. A drawback, however, it that these measurements are indirect and require careful interpretation to yield relevant information about the mechanisms of precipitation. Radar observations are an invaluable asset for the numerical forecast of precipitation, both for data assimilation, parametrization of subscale phenomena and model verification. This thesis aims at investigating new uses for polarimetric radar data in numerical weather prediction. The first part of this work is devoted to the design of an algorithm able to automatically detect the location and extent of the melting layer of precipitation , an important feature of stratiform precipitation, from vertical radar scans. This algorithm is then used to provide a detailed characterization of the melting layer, in several climatological regions, providing thus relevant information for the parameterization of melting processes and the evaluation of simulated freezing level heights. The second part of this work uses a multi-scale approach based on the multifractal framework to evaluate precipitation fields simulated by the COSMO weather model with radar observations. A climatological analysis is first conducted to relate multifractal parameters to physical descriptors of precipitation. A short-term analysis, that focuses on three precipitation events over Switzerland, is then performed. The results indicate that the COSMO simulations exhibit spatial scaling breaks that are not present in the radar data. It is also shown that a more advanced microphysics parameterization generates larger extreme values, and more discontinuous precipitation fields, which agree better with radar observations. The last part of this thesis describes a new forward polarimetric radar operator, able to simulate realistic radar variables from outputs of the COSMO model, taking into account most physical aspects of beam propagation and scattering. An efficient numerical scheme is proposed to estimate the full Doppler spectrum, a type of measurement often performed by research radars, which provides rich information about the particle velocities and turbulence. The operator is evaluated with large datasets from various ground and spaceborne radars. This evaluation shows that the operator is able to simulate accurate Doppler variables and realistic distributions of polarimetric variables in the liquid phase. In the solid phase, the simulated reflectivities agree relatively well with radar observations, but the polarimetric variables tend to be underestimated. A detailed sensitivity analysis of the radar operator reveals that, in the liquid phase, the simulated radar variables depend very much on the hypothesis about drop geometry and drop size distributions. In the solid phase, the potential of more advanced scattering techniques is investigated, revealing that these methods could help to resolve the strong underestimation of polarimetric variables in snow and graupel

From model to radar variables: a new forward polarimetric radar operator for COSMO

In this work, a new forward polarimetric radar operator for the COSMO numerical weather prediction (NWP) model is proposed. This operator is able to simulate measurements of radar reflectivity at horizontal polarization, differential reflectivity as well as specific differential phase shift and Doppler variables for ground based or spaceborne radar scans from atmospheric conditions simulated by COSMO. The operator includes a new Doppler scheme, which allows estimation of the full Doppler spectrum, as well a melting scheme which allows representing the very specific polarimetric signature of melting hydrometeors. In addition, the operator is adapted to both the operational one-moment microphysical scheme of COSMO and its more advanced two-moment scheme. The parameters of the relationships between the microphysical and scattering properties of the various hydrometeors are derived either from the literature or, in the case of graupel and aggregates, from observations collected in Switzerland. The operator is evaluated by comparing the simulated fields of radar observables with observations from the Swiss operational radar network, from a high resolution X-band research radar and from the dual-frequency precipitation radar of the Global Precipitation Measurement satellite (GPM-DPR). This evaluation shows that the operator is able to simulate an accurate Doppler spectrum and accurate radial velocities as well as realistic distributions of polarimetric variables in the liquid phase. In the solid phase, the simulated reflectivities agree relatively well with radar observations, but the simulated differential reflectivity and specific differential phase shift upon propagation tend to be underestimated. This radar operator makes it possible to compare directly radar observations from various sources with COSMO simulations and as such is a valuable tool to evaluate and test the microphysical parameterizations of the model.</p

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

A Nested K-Nearest Prognostic Approach for Microwave Precipitation Phase Detection over Snow Cover

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10 percent. In particular, the probability of precipitation detection and its solid phase increases by 11 and 8 percent, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surface