Probabilistic precipitation rate estimates with ground‐based radar networks

Abstract

The uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale. By using the WSR-88D radar network and gauge data sets across the conterminous US, an investigation of this subject has been carried out within the framework of the NOAA/NSSL ground radar-based Multi-Radar Multi-Sensor (MRMS) QPE system. A new method is proposed and called PRORATE for probabilistic QPE using radar observations of rate and typology estimates. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between radar reflectivity and the corresponding ''true'' precipitation. The model acknowledges the uncertainty arising from many factors operative at the radar measurement scale and from the correction algorithm. Ensembles of reflectivity-to-precipitation rate relationships accounting explicitly for precipitation typology were derived at a 5 min/1 km scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and type. The model components were estimated on the basis of a 1 year long data sample over the CONUS. This PQPE model provides the basis for precipitation probability maps and the generation of radar precipitation ensembles. Maps of the precipitation exceedance probability for specific thresholds (e.g., precipitation return periods) are computed. Precipitation probability maps are accumulated to the hourly time scale and compare favorably to the deterministic QPE. As an essential property of precipitation, the impact of the temporal correlation on the hourly accumulation is examined. This approach to PQPE can readily apply to other systems including space-based passive and active sensor algorithms.

Probabilistic precipitation rate estimates with space‐based infrared sensors

“…As described in Kirstetter et al . (), there is in theory no bias for a sufficiently large sample of independent data since it reflects the actual distribution of possible rate values under these observations. The output distribution of rates is broader when the T b ‐R relation is more uncertain.…”

“…As described in Kirstetter et al . (), various precipitation frequencies associated with return periods (e.g. 100 years) and accumulation periods (e.g.…”

“…Note that this finding is consistent with Kirstetter et al . () for radar PQPE. The log‐normal distribution is also used in many rainfall error models (e.g.…”

Evaluation and Uncertainty Estimation of the Latest Radar and Satellite Snowfall Products Using SNOTEL Measurements over Mountainous Regions in Western United States

“…To date, much effort has gone into evaluating precipitation retrievals by comparing with ground observations (e.g., [8][9][10][11][12]), while few studies have investigated the errors in snowfall retrievals. Kirstetter et al (2015) [13] performed a quantitative evaluation of the MRMS Reflectivity-Snow Water Equivalent (SWE) relationship, and noted significant underestimation relative to the precipitation gauges in Hydrometeorological Automated Data System (HADS). However, measurement errors for snow of HADS gauges frequently range from 20% to 50%, due to undercatch in windy conditions [14].…”

Local influence of south-east France topography and land cover on the distribution and characteristics of intense rainfall cells

“…After this first analysis, it was decided to focus on the pixels with intensity greater than 8 mm/hthe threshold for the definition of intense rainfall for Météo-France. However, it should be noted that the use of a unique Z-R relationship for the radar quantitative precipitation estimation, regardless of the precipitation type (for example, stratiform, convective, snow, hail) might create biases, as pointed out in Delrieu et al (2009) and Kirstetter et al (2015). This can affect the choice of the 8 mm/h threshold applied to identify rain cells, and its correction could be one of the overlooks of this work.…”