Uncertainty Model For Quantitative Precipitation Estimation Using Weather Radars

This paper introduces an uncertainty model for the quantitatively estimate precipitation using weather radars. The model considers various key aspects associated to radar calibration, attenuation, and the tradeoff between accuracy and radar coverage. An S-band-radar case study is presented to illustrate particular fractional-uncertainty calculations obtained to adjust various typical radar-calibration elements such as antenna, transmitter, receiver, and some other general elements included in the radar equation. This paper is based in “Guide to the expression of Uncertainty in measurement” [1] and the results show that the fractional uncertainty calculated by the model was 40 % for the reflectivity and 30% for the precipitation using the Marshall Palmer Z-R relationship

Short-term rainfall nowcasting: using rainfall radar imaging

As one of the most useful sources of quantitative precipitation measurement, rainfall radar analysis can be a very useful focus for research into developing methods for rainfall prediction. Because radar can estimate rainfall distribution over a wide range, it is thus very attractive for weather prediction over a large area. Short lead time rainfall prediction is often needed in meteorological and hydrological applications where accurate prediction of rainfall can help with flood relief, with agriculture and with event planning. A system of short-term rainfall prediction over Ireland using rainfall radar image processing is presented in this paper. As the only input, consecutive rainfall radar images are processed to predict the development of rainfall by means of morphological methods and movement extrapolation. The results of a series of experimental evaluations demonstrate the ability and efficiency of using our rainfall radar imaging in a nowcasting system

Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System(PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the IntegratedMultisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained

Uncertainty In Radar-rainfall Composite And Its Impact On Hydrologic Prediction For The Eastern Iowa Flood Of 2008

Key Points A significant potential source of error exists in mosaicked radar-rainfall maps. Different radar calibration offsets lead to misestimation of rainfall amounts. Systematic error in rainfall significantly affects hydrologic predictions. This study addresses a significant potential source of error that exists in radar-rainfall maps that are combined using data from multiple WSR-88D radars of the Next Generation Radar (NEXRAD) national network in the United States. This error stems from different radar calibration offsets that create a border within discontinuous rainfall fields at the equidistance zone among radars. The discontinuity in rainfall fields could lead to misestimation of rainfall over basins and subsequently, to significant errors in streamflow predictions through a hydrologic model. In this study, we produce enhanced radar-rainfall estimates (HN3) based on a novel approach that allows us to reduce the effects of the relative radar calibration bias. We use the relative bias information previously presented in a radar reflectivity comparison study. To investigate the effects of the relative bias adjustment, we evaluate the HN3 and Stage IV radar-rainfall by comparing them with rain gauge data and analyzing their ability to simulate streamflow for an extreme flood case. While the HN3 estimates are statistically comparable to the Stage IV estimates in the rain gauge data comparison, the borderline that identifies discontinuous rainfall fields disappears in the HN3 estimates. We performed hydrological simulations using a physically based, data-intensive, calibration-free, hillslope-link hydrologic model called CUENCAS and demonstrated CUENCAS\u27s ability to accurately simulate flows by comparing results with observed and predicted streamflow generated by the Sacramento (SAC) model. SAC is the operational flood forecast model that has been used by the National Weather Service since 1969, and it was extensively calibrated based on historical data. The simulation results show that the adjustment improves streamflow predictions in the regions where the misestimation of rainfall quantity is considerable. We conclude that systematic error arising from different calibration offsets in rainfall fields can significantly affect hydrologic predictions. ©2013. American Geophysical Union. All Rights Reserved