Search Results

You are looking at 1 - 2 of 2 items for

  • Author or Editor: Timothy J. Bellerby x
  • Refine by Access: All Content x
Clear All Modify Search
Timothy J. Bellerby and Jizhong Sun

Abstract

While current satellite techniques are theoretically capable of producing precipitation estimates to image pixel resolutions, significant uncertainty is present in such high-resolution products. This uncertainty is frequently difficult to characterize using scalar measures of additive error. This paper describes the development of a methodology to more fully represent the uncertainty in satellite precipitation retrievals. The methodology derives conditional probability distribution functions of rainfall on a pixel-by-pixel basis. This array of distribution functions is then combined with a simple model of the spatiotemporal covariance structure of the uncertainty in the precipitation field to stochastically generate an ensemble precipitation product. Each element of the ensemble represents an equiprobable realization of the precipitation field that is consistent with the original satellite data while containing a random element commensurate with the uncertainty in that field. The technique has been tested using data from the Tropical Rainfall Measuring Mission (TRMM) Texas and Florida Underflight Experiment (TEFLUN-B) field campaign.

Full access
Ali Behrangi, Bisher Imam, Kuolin Hsu, Soroosh Sorooshian, Timothy J. Bellerby, and George J. Huffman

Abstract

A new multiplatform multisensor satellite rainfall estimation technique is proposed in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to advect microwave (MW)-derived precipitation estimates along cloud motion streamlines and to further adjust the rainfall rates using local cloud classification. The main objective of the Rain Estimation using Forward-Adjusted advection of Microwave Estimates (REFAME) is to investigate whether inclusion of GEO-IR information can help to improve the advected MW precipitation rate as it gets farther in time from the previous MW overpass. The technique comprises three steps. The first step incorporates a 2D cloud tracking algorithm to capture cloud motion streamlines through successive IR images. The second step classifies cloudy pixels to a number of predefined clusters using brightness temperature (Tb) gradients between successive IR images along the cloud motion streamlines in combination with IR cloud-top brightness temperatures and textural features. A mean precipitation rate for each cluster is calculated using available MW-derived precipitation estimates. In the third step, the mean cluster precipitation rates are used to adjust MW precipitation intensities advected between available MW overpasses along cloud motion streamlines. REFAME is a flexible technique, potentially capable of incorporating diverse precipitation-relevant information, such as multispectral data. Evaluated over a range of spatial and temporal scales over the conterminous United States, the performance of the full REFAME algorithm compared favorably with products incorporating either no cloud tracking or no intensity adjustment. The observed improvements in root-mean-square error and especially in correlation coefficient between REFAME outputs and ground radar observations demonstrate that the new approach is effective in reducing the uncertainties and capturing the variation of precipitation intensity along cloud advection streamlines between MW sensor overpasses. An extended REFAME algorithm combines the adjusted advected MW rainfall rates with infrared-derived precipitation rates in an attempt to capture precipitation events initiating and decaying during the interval between two consecutive MW overpasses. Evaluation statistics indicate that the extended algorithm is effective to capture the life cycle of the convective precipitation, particularly for the interval between microwave overpasses in which precipitation starts or ends.

Full access