Application Of Radar Data Research Articles

Error Analysis and Modeling of GPM Dual-Frequency Precipitation Radar Near-Surface Rainfall Product

Abstract The error characterization of rainfall products of spaceborne radar is essential for better applications of radar data, such as multisource precipitation data fusion and hydrological modeling. In this study, we analyzed the error of the near-surface rainfall product of the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement Mission (GPM) and modeled it based on ground C-band dual-polarization radar (CDP) data with optimization rainfall retrieval. The comparison results show that the near-surface rainfall data were overestimated by light rain and slightly underestimated by heavy rain. The error of near-surface rainfall of the DPR was modeled as an additive model according to the comparison results. The systematic error of near-surface rainfall was in the form of a quadratic polynomial, while the systematic error of stratiform precipitation was smaller than that of convective precipitation. The random error was modeled as a Gaussian distribution centered from −1 to 0 mm h−1. The standard deviation of the Gaussian distribution of convective precipitation was 1.71 mm h−1, and the standard deviation of stratiform precipitation was 1.18 mm h−1, which is smaller than that of convective precipitation. In view of the precipitation retrieval algorithm of DPR, the error causes were analyzed from the reflectivity factor (Z) and the drop size distribution (DSD) parameters (Dm, Nw). The high accuracy of the reflectivity factor measurement results in a small systematic error. Importantly, the negative bias of Nw was very obvious when the rain type was convective precipitation, resulting in a large random error. Significance Statement This study first compares the total and different rain types of near-surface rainfall measured by DPR and ground-based radar CDP, then separates the error of DPR near-surface rainfall into systematic and random errors and analyzes the possible causes of the error. The purpose of this study is to better apply the error model to applications such as optimal data fusion and hydrological modeling, and the analysis of the error can also provide a basis for improving the spaceborne precipitation retrieval algorithm.

A High-precision and Fast Solution Method of Gamma Raindrop Size Distribution based on 0-Moment and 3-Moment in South China

AbstractAccording to the high accuracy linear shape-slope (μ-Λ) relationship observed by several 2-Dimensional-Video-Distrometers (2DVD) in South China, a high-precision and fast solution method of gamma (Γ) raindrop size distribution (RSD) function based on the zeroth order moment (M0) and the third order moment (M3) of RSD has been proposed. The 0-moment (M0) and 3-moment (M3) of RSD can be easily calculated from rain mass mixing ratio (Qr) and total number concentration (Ntr) simulated by the two-moment (2M) microphysical scheme, respectively. Three typical heavy rainfall processes and all RSD samples observed during 2019 in South China were selected to verify the accuracy of the method. Compared to the current widely used exponential RSD with a fixed shape parameter of zero in 2M microphysical scheme, the Γ RSD function using the linear constrained gamma (C-G) method agreed better with the Γ fit RSD from 2DVD observations. The characteristic precipitation parameters (e.g., rain rate, M2, M6 and M9) obtained by the proposed method are generally consistent with the parameters calculated by Γ fit RSD from 2DVD observations. The proposed method has effectively solved the problem that the shape parameter in the 2M microphysical scheme set to a constant, so that the Γ RSD functions are closer to the observations and have obviously smaller errors. This method has a good potential to be applied to the 2M microphysical schemes to improve the simulation of heavy precipitation in South China, but also paves the way for in-depth applications of radar data in numerical weather prediction models.

Application of radar data for the analysis of the rainfall-runoff process in an urban catchment

Application of radar data for the analysis of the rainfall-runoff process in an urban catchment

Land Use Classification and Analysis Using Radar Data Mining in Ethiopia

Land use classification in tropical areas, is hindered by frequent cloud cover which limits the availability of optical satellite data. Satellite-borne radar is a possible alternative to optical data for land use classification in tropical areas. However, radar data is affected by noise (i.e., speckle) that must be minimized before its use in land classification. Median, Lee-Sigma, and Gamma-MAP de-speckling techniques were applied to Fine Beam, Dual polarization (FBD) PALSAR radar data acquired over central Ethiopia. Each of the de-speckled images were then subjected to supervised classification using Maximum Likelihood, C4.5, Multilayer Perceptron and Stacking techniques. Validation results indicated that de-speckling techniques improved classification accuracy by up to 25%, 20% and 16% using Gamma-MAP, Median and Lee-Sigma respectively. Gamma-MAP de-speckling in combination with the Multilayer perceptron classifier achieved the best overall classification accuracy at 91.2%. This study proved the importance of radar data as an alternative source of information for land use classification in the tropics. Further research should focus on the application of radar data for forest fire detection and crop classification. The use of fully polarized radar data has the potential to further improve the proposed land use classification in tropical countries.

A test pattern identification algorithm and its application to CINRAD/SA(B) data

A variety of faulty radar echoes may cause serious problems with radar data applications, especially radar data assimilation and quantitative precipitation estimates. In this study, “test pattern” caused by test signal or radar hardware failures in CINRAD (China New Generation Weather Radar) SA and SB radar operational observations are investigated. In order to distinguish the test pattern from other types of radar echoes, such as precipitation, clear air and other non-meteorological echoes, five feature parameters including the effective reflectivity data percentage (R Z), velocity RF (range folding) data percentage (R RF), missing velocity data percentage (R M), averaged along-azimuth reflectivity fluctuation $$\left( {R_{N_{r,Z} } } \right)$$ and averaged along-beam reflectivity fluctuation $$\left( {R_{N_{a,Z} } } \right)$$ are proposed. Based on the fuzzy logic method, a test pattern identification algorithm is developed, and the statistical results from all the different kinds of radar echoes indicate the performance of the algorithm. Analysis of two typical cases with heavy precipitation echoes located inside the test pattern are performed. The statistical results show that the test pattern identification algorithm performs well, since the test pattern is recognized in most cases. Besides, the algorithm can effectively remove the test pattern signal and retain strong precipitation echoes in heavy rainfall events.

Comparison of raingage and WSR-88D Stage III precipitation data over the Texas-Gulf basin

Precipitation is one of the most important hydrologic model inputs and is characterized by spatial and temporal variability. Traditionally, raingage networks have provided precipitation data for hydrologic studies. Weather radars like the WSR-88D estimate precipitation with high spatial and temporal sampling frequencies, but radar estimates suffer from errors due to several reasons. Assessment of the quality of radar precipitation data is necessary before the application of radar data in hydrologic studies and is important for improving radar data processing algorithms. Accuracy assessment of Stage III precipitation data from the WSR-88D network over the Texas-Gulf basin was performed for the period 1995–99 using 24-h accumulations from 545 raingages. Sampling differences between point raingage measurements and areal radar estimates were ignored. WSR-88D underestimated the five-year precipitation at a vast majority of the raingages. Difference in the total precipitation depth between radar and raingage was within ±500 mm only at 42% of the raingages; the root mean squared difference was more than 10 mm at 78% of the raingages. Such large differences between both rainfall data sources will have significant implications for the hydrologic applications of WSR-88D Stage III data. Radar estimation efficiency was better than 0.50 at 71% of the First Order/FAA raingages and 41% of the cooperative observer raingages. Radar performance varied significantly over the years, and, in general, improvement in radar performance was observed, which is consistent with the on-going developments in WSR-88D data processing algorithms; however, an overestimation trend was identified during 1998–99. Evaluation of the quality of WSR-88D Stage III data and making necessary corrections are important before their application in hydrologic studies.

Accessing and Using Digitized Radar with Microcomputers

ABSTRACT RADAR provides a means of monitoring precipitation over large areas in real-time. National Weather Service network radars have digital video integrator processors that convert radar echoes into digital information based upon echo strength. Commercial microcomputers were configured and programmed to receive this digital radar information. Rainfall rates determined from the radar data were used to estimate soil moisture changes and rainfall runoff hydrographs. Successful implementation of equipment and algorithms demonstrate that recent advances in microcomputer technology allow application of radar data in real-time.

Earth-Based Radar Studies of Planetary Surfaces And Atmospheres

Radar has been used as a remote sensing tool since 1946 when the first echoes from the moon were detected by ground-based radar systems. Since then the sensitivity of our most powerful radar systems has increased by a factor of about 1012, and radar studies of Mercury, Venus, Earth's moon, Mars, the Galilean moons of Jupiter, the rings of Saturn, and a dozen asteroids have been made. Specific studies range from simple measurements of distance or reflectivity to detailed maps or images of reflectivity, altimetry, and various other model parameters. This paper presents a short review of the more recent observations, and summarizes current research activities associated with each body. An attempt is made to anticipate the observational data set that should be available during the next decade. Such data will be useful for remote sensing applications, either directly or combined with data from other programs. The usefulness of the radar data is also dependent upon theoretical models that explain the observed scattering. Such models often are dependent upon parameters that are not measurable by radar. Likewise, models that predict other physical measurements may be influenced by parameters that are easily measured by radar. Thus many more exciting applications of radar data should become apparent as further observations are made by other techniques and as models are developed to explain the observed quantities.