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

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

The development of rainfall retrievals from radar at Darwin

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.5194/amt-14-53-2021The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dualpolarization quantities was wavelength dependent for 1 10 mm h−1 . Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1 10 mm h−1 (deeper convective rain).Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under contract DE-AC02-06CH11357. This work has been supported by the Office of Biological and Environmental Research (OBER) of the U.S. Department of Energy (DOE) as part of the Climate Model Development and Validation activity. NOAA PSL contributes effort with funding from the Weather Program Office’s Precipitation Prediction Grand Challenge. The development of the Python ARM radar toolkit was funded by the ARM program part of the Office of Biological and Environmental Research (OBER) of the U.S. Department of Energy (DOE). The work from Monash University and the Bureau of Meteorology was partly supported by the U.S. Department of Energy Atmospheric Systems Research Program through the grant DE-SC0014063. BD contributions are supported by the U.S. Department of Energy Atmospheric Systems Research Program through the grant DE-SC0017977

Exploitation of X-band weather radar data in the Andes high mountains and its application in hydrology: a machine learning approach

Rainfall in the tropical Andes high mountains is paramount for understanding complex hydrological and ecological phenomena that take place in this distinctive area of the world. Here, rainfall drives imminent hazards such as severe floods, rainfall-induced landslides, different types of erosion, among others. Nonetheless, sparse and uneven distributed rain gauge networks as well as low- resolution satellite imagery are not sufficient to capture its high variability and complex dynamics in the irregular topography of high mountains at appropriate temporal and spatial scales. This results in both, a lack of knowledge about rainfall patterns, as well as a poor understanding of rainfall microphysics, which to date are largely underexplored in the tropical Andes. Therefore, this investigation focuses on the deployment and exploitation of single-polarization (SP) X-band weather radars in the Andean high mountain regions of southern Ecuador, applicable to quantitative precipitation estimation (QPE) and discharge forecasting. This work leverages radar rainfall data by exploring a machine learning (ML) approach. The main aims of the thesis were: (i) The deployment of a first X-band weather radar network in tropical high mountains, (ii) the physically-based QPE of X-band radar retrievals, (iii) the optimization of radar QPE by using a ML-based model and (iv) a discharge forecasting application using a ML-based model and SP X-band radar data. As a starting point, deployment of the first weather radar network in tropical high mountains was carried out. A complete framework for data transmission was set for communication among the network. The highest radar in the network (4450 m a.s.l.) was selected in this study for exploiting the potential of SP X-band radar data in the Andes. First and foremost, physically-based QPE was performed through the derivation of Z-R relationships. For this, data from three disdrometers at different geographic locations and elevation were used. Several rainfall events were selected in order to perform a classification of rainfall types based on the mean volume diameter (Dm [mm]). Derived Z-R relations confirmed the high variability in their parameters due to different rainfall types in the study area. Afterwards, the optimization of radar QPE was pursued by using a ML approach as an alternative to the common physically-based QPE method by means of the Z-R relation. For this, radar QPE was tackled by using two different approaches. The first one was conducted by implementing a step-wise approach where reflectivity correction is performed in a step-by-step basis (i.e., clutter removal, attenuation correction). Finally a locally derived Z-R relationship was applied for obtaining radar QPE. Rain gauge-bias adjustment was neglected because the availability of rain gauge data at near-real time is limited and infrequent in the study area. The second one was conducted by an implementation of a radar QPE model that used the Random Forest (RF) algorithm and reflectivity derived features as inputs for the model. Finally, the performances of both models were compared against rain gauge data. The results showed that the ML-based model outperformed the step-wise approach, making it possible to obtain radar QPE without the need of rain gauge data after the model was implemented. It also allowed to extend the useful range of the radar image (i.e., up to 50 km). Radar QPE can be generally used as input for discharge forecasting models if available. However, one could expect from ML-based models as RF, the ability to map radar data to the target variable (discharge) without any intermediate step (e.g., transformation from reflectivity to rainfall rate). Thus, a comparison for discharge forecasting was performed between RF models that used different input data type. Input data for the relevant models were obtained either from native reflectivity records (i.e., reflectivity corrected from unrealistic measurements) or derived radar-rainfall data (i.e., radar QPE). Results showed that both models performed alike. This proved the suitability of using native radar data (reflectivity) for discharge forecasting in mountain regions. This could be extrapolated in the advantages of deploying radar networks and use their information directly to fed early-warning systems regardless of the availability of rain gauges at ground. In summary, this investigation (i) participated on the deployment of the first weather radar network in tropical high mountains, (ii) significantly contributed to a deeper understanding of rainfall microphysics and its variability in the high tropical Andes by using disdrometer data and (iii) exploited, for the very first time, the native X-band radar reflectivity as a suitable input for ML-based models for both, optimized radar QPE and discharge forecasting. The latter highlighted the benefits and potentials of using a ML approach in radar hydrology. The research generally accounted for ground monitoring limitations commonly found in mountain regions and provided a promising alternative with leveraging the cost-effective X-band technology in the steep terrain of the Andean Cordillera

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

Land cover and forest mapping in boreal zone using polarimetric and interferometric SAR data

Remote sensing offers a wide range of instruments suitable to meet the growing need for consistent, timely and cost-effective monitoring of land cover and forested areas. One of the most important instruments is synthetic aperture radar (SAR) technology, where transfer of advanced SAR imaging techniques from mostly experimental small test-area studies to satellites enables improvements in remote assessment of land cover on a global scale. Globally, forests are very suitable for remote sensing applications due to their large dimensions and relatively poor accessibility in distant areas. In this thesis, several methods were developed utilizing Earth observation data collected using such advanced SAR techniques, as well as their application potential was assessed. The focus was on use of SAR polarimetry and SAR interferometry to improve performance and robustness in assessment of land cover and forest properties in the boreal zone. Particular advances were achieved in land cover classification and estimating several key forest variables, such as forest stem volume and forest tree height. Important results reported in this thesis include: improved polarimetric SAR model-based decomposition approach suitable for use in boreal forest at L-band; development and demonstration of normalization method for fully polarimetric SAR mosaics, resulting in improved classification performance and suitable for wide-area mapping purposes; establishing new inversion procedure for robust forest stem volume retrieval from SAR data; developing semi-empirical method and demonstrating potential for soil type separation (mineral soil, peatland) under forested areas with L-band polarimetric SAR; developing and demonstrating methodology for simultaneous retrieval of forest tree height and radiowave attenuation in forest layer from inter-ferometric SAR data, resulting in improved accuracy and more stable estimation of forest tree height

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

A virtual centre at the interface of basic and applied weather and climate research

The Hans-Ertel Centre for Weather Research is a network of German universities, research institutes and the German Weather Service (Deutscher Wetterdienst, DWD). It has been established to trigger and intensify basic research and education on weather forecasting and climate monitoring. The performed research ranges from nowcasting and short-term weather forecasting to convective-scale data assimilation, the development of parameterizations for numerical weather prediction models, climate monitoring and the communication and use of forecast information. Scientific findings from the network contribute to better understanding of the life-cycle of shallow and deep convection, representation of uncertainty in ensemble systems, effects of unresolved variability, regional climate variability, perception of forecasts and vulnerability of society. Concrete developments within the research network include dual observation-microphysics composites, satellite forward operators, tools to estimate observation impact, cloud and precipitation system tracking algorithms, large-eddy-simulations, a regional reanalysis and a probabilistic forecast test product. Within three years, the network has triggered a number of activities that include the training and education of young scientists besides the centre's core objective of complementing DWD's internal research with relevant basic research at universities and research institutes. The long term goal is to develop a self-sustaining research network that continues the close collaboration with DWD and the national and international research community

A review of high impact weather for aviation meteorology

This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility, aerosol/ash loading, ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can also play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in situ instruments at the surface and in the atmosphere, as well as aircraft and Unmanned Aerial Vehicles mounted sensors, are becoming more common. At smaller time and space scales (e.g., < 1 km), meteorological forecasts from NWP models need to be continuously improved for accurate physical parameterizations. Aviation weather forecasts also need to be developed to provide detailed information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space