Rainfall radar and satellite uncertainty
Typical quantitative results of rainfall uncertainty studies: Radar and Satellite.
This table originated from McMillan et al. (2012) but is now open to the community to add to and use as a resource.
RMSE = root mean square error; SD = standard deviation
|Uncertainty Type||Estimation Method||Magnitude||Location||Reference|
|Error between radar estimate and gauge network||Radar RMSE with respect to 30 raingauges||10 % for storms >30 mm after radar bias correction using high quality rain gauge data; when all gauges were used for bias correction without prior quality control RMSE was 10-40 %||ARS Goodwin Creek experimental watershed, Mississippi, USA||Steiner et al. (1999)|
|Error between radar estimate and gauge network||Standard error of residuals compared with 8 rain gauges in 2 km2 area||50% (low relief) at 4 mm/15 min rain rate; presented graphically for rain rates 0.4-10 mm/15 min||Brue catchment, UK (135 km2). 20-250 m a.s.l., temperate climate, orographic rainfall.||Wood et al. (2000)|
|Standard error of residuals compared with 49 rain gauges in 135 km2 area||55 % at 2km resolution, 60 % at 5 km resolution, for rain rate 4 mm/15 min; presented graphically for rain rates 0.2-8 mm/15 min|
|Error between radar (WSR-88D) estimate and gauge network||SD of the stochastic component of multiplicative error||Conditioned on distance from radar, timescale of observation & season; asymptotic SD at high rainfall rates in the range 0.1-0.7, typically 0.5 for hourly data||Oklahoma, USA. Rainfall 800 mm yr-1, dominated by midlatitude convective systems.||Ciach et al. (2007)|
|Error between radar (S-band) estimate and gauge network||SD of residuals||Approx. 0.3 (proportion of mean rain rate) for hourly data over 0-100 km distance from radar; values also given for 1, 2, 6, 12 hours & 0-50, 50-100, 0-100 km distances||Cévennes-Vivarais region, France. 200 km *160 km convective and frontal rainfall.||Kirstetter et al. (2010)|
|Error between radar (WSR-88D) estimate and gauge network||SD of residuals (2 research gauge networks)||0.48 (hourly, 8 km resolution), 1.07 (hourly, 1 km resolution), proportion of mean rain rate; values also given for 15 min, 1 hour at scales 0.5, 1, 2, 4, 8 km||Iowa, USA||Seo & Krajewski (2010); raingauge networks used paired gauges at all sites|
|Error between radar (X-band) estimate and gauge network||Mean and SD of bias for pixel-based comparison between 2 radars and 20 gauges.||Using a Z-R relationship to estimate rainfall, the mean bias for the 2 radars was -0.24, -0.27; with SD of the relative error 0.46, 0.48.||Southwest Oklahoma, USA. Raingauges – radar distance up to 35 km. Study used 4 storm events of heavy/ broken squall lines with embedded convective cells.||Vieux and Imgarten (2011)|
|Bias in estimates of surface rain rate from TRMM (Tropical Rainfall Measuring Mission)||Bayesian modelling approach to estimate SD of each parameter in algorithm used to calculate surface rain rate||SD of combined multiplicative bias in rain rate presented graphically as a function of rain rate: 40-60% at rates up to 18 mm h-1, 150 % at 25 mm h-1,||All oceanic pixels for 10 TRMM orbits||L’Ecuyer and Stephens (2002)|
|Bias of two NASA satellite products (infrared & passive microwave)||Mean & variance in multiplicative bias at hourly timesteps & 0.25º resolution compared with ground radar||Mean multiplicative hourly bias 0.35-1.09 (with SD of 0.73-0.84) over 4-month study period.||Oklahoma, USA. Southern Plains, 95-100°W, 34-37°N.||Hossain & Anagnostou (2006)|
Ciach, G.J., Krajewski, W.F., Villarini, G., 2007. Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data. Journal of Hydrometeorology, 8(6): 1325-1347.
Hossain, F., Anagnostou, E.N., 2006. Assessment of a multidimensional satellite rainfall error model for ensemble generation of satellite rainfall data. IEEE Geoscience and Remote Sensing Letters, 3(3): 419-423.
Kirstetter, P.E., Delrieu, G., Boudevillain, B., Obled, C., 2010. Toward an error model for radar quantitative precipitation estimation in the Cevennes-Vivarais region, France. Journal of Hydrology, 394(1-2): 28-41.
L’Ecuyer, T. S., and G. L. Stephens, 2002. An uncertainty model for Bayesian Monte Carlo retrieval algorithms: Application to the TRMM observing system. Quart. J. Roy. Meteor. Soc.,128, 1713–1737.
McMillan, H., Krueger, T., Freer, J., 2012. Benchmarking observational uncertainties for hydrology: Rainfall, river discharge and water quality. Hydrological Processes 26(26): 4078–4111
Seo, B.C., Krajewski, W.F., 2010. Scale dependence of radar rainfall uncertainty: Initial evaluation of NEXRAD's new super-resolution data for hydrologic applications. Journal of Hydrometeorology, 11(5): 1191-1198.
Steiner, M., Smith, J.A., Burges, S.J., Alonso, C.V., Darden, R.W., 1999. Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation. Water Resources Research, 35(8): 2487-2503.
Vieux, B.E., Imgarten, J.M., 2011. On the scale-dependent propagation of hydrologic uncertainty using high-resolution X-band radar rainfall estimates. Atmospheric Research. 103: 96-105.
Wood, S.J., Jones, D.A., Moore, R.J., 2000. Accuracy of rainfall measurement for scales of hydrological interest. Hydrology and Earth System Sciences, 4(4): 531-543.