Haonan Chen

Evaluation of Multisensor Quantitative Precipitation Estimation in Russian River Basin

AbstractAn important goal of combining weather radar with rain gauge data is to provide reliable estimates of rainfall rate and accumulation and to further identify intense precipitation and issue flood warnings. Scanning radars provide the ability to observe precipitation over wider areas within shorter timeframes compared to rain gauges, leading to improved situational awareness and more accurate and reliable warnings of future precipitation and flooding events. The focus of this study is on evaluating the performance of the multi-radar multi-sensor (MRMS) system with and without the impact of a local gap filling radar. The challenge of using radar and rain gauges to provide accurate rainfall estimates in complex terrain is investigated. The area of interest is the Russian River basin north of San Francisco, CA, which lies within the National Oceanic and Atmospheric Administration (NOAA) Hydrometeorology Testbed (HMT). In this complex mountainous terrain, the challenge of obtaining reliable quantitative...

Overview of the D3R Observations during the IFloodS Field Experiment with Emphasis on Rainfall Mapping and Microphysics

AbstractThe NASA dual-frequency, dual-polarization Doppler radar (D3R) was deployed as part of the GPM Iowa Flood Studies (IFloodS) ground validation field campaign from 1 May through 15 June 2013. The D3R participated in a multi-instrument targeted investigation of convective initiation and hydrological response in the midwestern United States. An overview of the D3R’s calibration and observations is presented. A method for attenuation correction of Ka-band observations using Ku-band results is introduced. Dual-frequency ratio estimates in stratiform rain and ice are presented and compared with theoretical values. Ku-band quantitative precipitation estimation results are validated against IFloodS ground instruments.

High resolution rainfall mapping in the Dallas-Fort Worth urban demonstration network

Flooding is one of the most catastrophic disasters in the world. Radar rainfall estimation for flash flood forecasting in small, urban catchments is an important accomplishment of CASA. A Kdp (specific differential propagation phase) based rainfall algorithm was developed using the Kdp values of X-band radars. The performance of this rainfall algorithm is evaluated for all major rainfall events using a gauge network in the center of CASA IP1 test bed for a 5- year period. The cross comparison with gauge estimates shows great improvement compared to the current state-of-the-art. Since the beginning of 2012, CASA has been involved in developing the first urban weather demonstration network in Dallas-Fort Worth (DFW) area. This paper will summarize the performance of the radar rainfall product in the IP1 network. In addition, the implementation of CASA QPE (quantitative precipitation estimation) system in DFW Urban Demonstration Network will also be presented.

Validation concepts with ground radars for global precipitation mission during the post launch era

The GPM core satellite will be ready to launch in February 2014, less than 7 months after the end of IGARSS2013 symposium. In the pre-launch era, several international validation experiments such as LPVEX (Light Precipitation Validation Experiment), MC3E (Midlatitude Continental Convective Clouds Experiment), and IFloodS (Iowa Flood Studies) have already generated a substantial set of measurements that continue to contribute to the development and test of pre-launch GPM algorithms. Following launch, it is expected that GPM ground validation will focus on evaluating precipitation data products, generated by a constellation of GPM satellites as well as assumptions made in algorithms. This paper presents simple concepts of dual-polarization radar observation strategies and products that can be generated for the post launch era of the GPM program, especially when there are satellite overpasses. The use of microphysical retrievals from ground-based radars and in-situ observations for validating the retrievals from space based observations is the main focus of this paper.

Urban flash flood applications of high-resolution rainfall estimation by X-band dual-polarization radar network

Flooding in general, especially the urban flash flooding is one of the most destructive nature hazards. Rainfall estimates from radar network are often used as input to various hydrological models for further flood warning and mitigations. The X-band dual-polarization radar network developed by the United States National Science Foundation Engineering Research Center (NSF-ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) has shown great improvement to radar based Quantitative Precipitation Estimation (QPE), through many years of experimental validation studies. QPE and rainfall nowcasting are important goals of CASA X-band dual-polarization radar networks. This paper presents an overview of CASA QPE and nowcasting methodology. In addition, 20 rainfall events collected from the Oklahoma test best during the past 3 years are used to evaluate the networked radar rainfall products. Cross validation with a gauge network using these 20 events’ data shows that the estimates of instantaneous rain rate, 5-minute,10- minute, and hourly rainfall have normalized standard error of about 47.57%, 40.03%, 34.61% and 24.78% , respectively, whereas a low bias of about -3.83%, -2.83%,-2.77% and -3.45% respectively. These evaluation results demonstrate great improvement compared to the current state-of-the-art. The paper also deals with the potential role of these highresolution rainfall products for flash floods warning and mitigation.

Deployment and performance of the NASA D3R during the GPM OLYMPEx field campaign

The NASA D3R was successfully deployed and operated throughout the NASA OLYMPEx field campaign. A differential phase based attenuation correction technique has been implemented for D3R observations. Hydrometeor classification has been demonstrated for five distinct classes using Ku-band observations of both convection and stratiform rain. The stratiform rain hydrometeor classification is compared against LDR observations and shows good agreement in identification of mixed-phase hydrometeors in the melting layer.

Rainfall estimation from spaceborne and ground based radars using neural networks

Neural network (NN) is a nonparametric method to represent the relation between radar measurements and rainfall rate. The relation is derived directly from a dataset consisting of radar measurements and rain gauge measurements. Tropical Rainfall measuring Mission (TRMM) Precipitation Radar (PR) is known to be the first observation platform for mapping precipitation over the tropics. TRMM measured rainfall makes a significant contribution to the study of precipitation distribution over the globe in the tropics. Ground validation (GV) is a critical component in the TRMM system. However, the ground sensing systems have quite different characteristics from TRMM in terms of resolution, scale, sampling, viewing aspect, and uncertainties in the sensing environments. In this paper a novel hybrid NN model is presented to train ground radars for rainfall estimation using rain gauge data and subsequently the trained ground radar rainfall estimation to train TRMM/PR observation based neural networks. This hybrid NN model provides a mechanism to link between gauges on the ground, the ground radar observations and the TRMM/PR observations. The dual-polarization radar measurements from a ground WSR-88DP site in Dallas-Fort Worth region and local rain gauge data will be used for the demonstration purpose. The performance of the rainfall product derived for TRMM PR is then compared against TRMM standard rainfall products. In addition, a direct gauge comparison study is done to examine the improvement brought in by this hybrid neural networks approach.

Deployment and performance of NASA D3R during GPM IPHEx field campaign

In order to investigate how well observations from precipitation-monitoring satellites match up to the best estimate of the true precipitation measured at ground level and how to use the collected precipitation data to evaluate models that describe and predict the hydrology, the Integrated Precipitation and Hydrology Experiment (IPHEx) was conducted in the southern Appalachian Mountains in the eastern United States from May 1 to June 15, 2014. The NASA dual-frequency dual-polarization Doppler radar (D3R), co-located with NASA NPOL radar, was deployed as part of the IPHEx field campaign to characterize precipitation properties at Ku- and Ka-band frequencies. This paper presents the deployment and performance of D3R during the IPHEx field experiment. Sample observations will be presented, with particular attention paid to cross-comparison between D3R and NPOL.

Deployment and performance of the NASA D3R during GPM IFloods field campaign

The Iowa Flood Studies (IFloodS) field experiment was conducted to better understand the strengths and limitations of Global Precipitation Measurement (GPM) mission satellite products in the context of hydrologic applications. The NASA dual-frequency dual-polarization Doppler radar (D3R), designed as part of the GPM ground validation program, participated in the IFloodS field campaign to characterize precipitation properties at Ku- and Ka-band frequencies. This paper presents the deployment of the D3R and summarizes the D3R observations during the IFloodS field campaign. The quality of the D3R measurements is evaluated by comparing with the NASA NPOL S-band radar observations. In addition, the capability for rainfall estimation using the D3R is also described and validated using ground gauge measurements.

Chapter 15 – Real-Time Wind Velocity Retrieval in the Precipitation System Using High-Resolution Operational Multi-radar Network

Abstract High-impact wind phenomena such as tornadoes and microburst outflows can form and dissipate within a few minutes, and can be relatively localized and fast-moving. Monitoring such events is critical for issuing timely severe weather warnings in an operational environment. Multiple-Doppler radar techniques have been shown to be useful in retrieving the three-dimensional wind velocity components within thunderstorms and/or mesoscale convective systems in research environments. However, it is difficult to use the traditional operational National Weather Service (NWS) radar (S or C band) network to perform reliable and efficient multiple-Doppler retrievals due to its coarse resolution and coverage limitations. In recent years, the center for Collaborative Adaptive Sensing of the Atmosphere (CASA) has introduced a dense network-sensing methodology to improve weather sensing and forecasting by using high-resolution X-band radars. This chapter presents the application of an operational CASA radar network with regard to vector wind velocity retrieval. An overview of the multiple-Doppler retrieval techniques is provided with a focus on the implementation in the high-resolution CASA radar networks. Sample real-time wind retrieval products in the presence of tornadoes, high linear winds, and downbursts, are presented. The potential of these products as real-time emergency weather warning tools is demonstrated.