TY - JOUR
T1 - Radar rainfall estimation for ground validation studies of the tropical rainfall measuring mission
AU - Ciach, Grzegorz J.
AU - Krajewski, Witold F.
AU - Anagnostou, Emmanouil N.
AU - Baeck, Mary L.
AU - Smith, James A.
AU - McCollum, Jeffrey R.
AU - Kruger, Anton
PY - 1997/6
Y1 - 1997/6
N2 - This study presents a multicomponent rainfall estimation algorithm, based on weather radar and rain gauge network, that can be used as a ground-based reference in the satellite Tropical Rainfall Measuring Mission (TRMM). The essential steps are constructing a radar observable, its nonlinear transformation to rainfall, interpolation to rectangular grid, constructing several timescale accumulations, bias adjustment, and merging of the radar rainfall estimates and rain gauge data. Observations from a C-band radar in Darwin, Australia, and a local network of 54 rain gauges were used to calibrate and test the algorithm. A period of 25 days was selected, and the rain gauges were split into two subsamples to apply cross-validation techniques. A Z-R relationship with continuous range dependence and a temporal interpolation scheme that accounts for the advection effects is applied. An innovative methodology was used to estimate the algorithm controlling parameters. The model was globally optimized by using an objective function on the level of the final products. This is equivalent to comparing hundreds of Z-R relationships using a uniform and representative performance criterion. The algorithm performance is fairly insensitive to the parameter variations around the optimum. This suggests that the accuracy limit of the radar rainfall estimation based on power-law Z-R relationships has been reached. No improvement was achieved by using rain regime classification prior to estimation.
AB - This study presents a multicomponent rainfall estimation algorithm, based on weather radar and rain gauge network, that can be used as a ground-based reference in the satellite Tropical Rainfall Measuring Mission (TRMM). The essential steps are constructing a radar observable, its nonlinear transformation to rainfall, interpolation to rectangular grid, constructing several timescale accumulations, bias adjustment, and merging of the radar rainfall estimates and rain gauge data. Observations from a C-band radar in Darwin, Australia, and a local network of 54 rain gauges were used to calibrate and test the algorithm. A period of 25 days was selected, and the rain gauges were split into two subsamples to apply cross-validation techniques. A Z-R relationship with continuous range dependence and a temporal interpolation scheme that accounts for the advection effects is applied. An innovative methodology was used to estimate the algorithm controlling parameters. The model was globally optimized by using an objective function on the level of the final products. This is equivalent to comparing hundreds of Z-R relationships using a uniform and representative performance criterion. The algorithm performance is fairly insensitive to the parameter variations around the optimum. This suggests that the accuracy limit of the radar rainfall estimation based on power-law Z-R relationships has been reached. No improvement was achieved by using rain regime classification prior to estimation.
UR - http://www.scopus.com/inward/record.url?scp=0031399273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031399273&partnerID=8YFLogxK
U2 - 10.1175/1520-0450-36.6.735
DO - 10.1175/1520-0450-36.6.735
M3 - Article
AN - SCOPUS:0031399273
SN - 0894-8763
VL - 36
SP - 735
EP - 747
JO - Journal of Applied Meteorology
JF - Journal of Applied Meteorology
IS - 6
ER -