Climatology-Calibrated Precipitation Analysis at Fine Scales: Statistical Adjustment of Stage IV toward CPC Gauge-Based Analysis

Abstract

Two widely used precipitation analyses are the Climate Prediction Center (CPC) unified global daily gauge analysis and Stage IV analysis based on quantitative precipitation estimate with multisensor observations. The former is based on gauge records with a uniform quality control across the entire domain and thus bears more confidence, but provides only 24-h accumulation at ⅛° resolution. The Stage IV dataset, on the other hand, has higher spatial and temporal resolution, but is subject to different methods of quality control and adjustments by different River Forecasting Centers. This article describes a methodology used to generate a new dataset by adjusting the Stage IV 6-h accumulations based on available joint samples of the two analyses to take advantage of both datasets. A simple linear regression model is applied to the archived historical Stage IV and the CPC datasets after the former is aggregated to the CPC grid and daily accumulation. The aggregated Stage IV analysis is then adjusted based on this linear model and then downscaled back to its original resolution. The new dataset, named Climatology-Calibrated Precipitation Analysis (CCPA), retains the spatial and temporal patterns of the Stage IV analysis while having its long-term average and climate probability distribution closer to that of the CPC analysis. The limitation of the methodology at some locations is mainly associated with heavy to extreme precipitation events, which the Stage IV dataset tends to underestimate. CCPA cannot effectively correct this because of the linear regression model and the relative scarcity of heavy precipitation in the training data sample.

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012)

“…Stage IV displays a negative bias for summer when compared to GHCN-D and PRISM (≈ −19 %) and comparable with the bias observed for winter (−12 %). Differences remain significant despite the fact that Stage IV uses the PRISM/Mountain Mapper algorithm that combines automated rain gauge observations and PRISM monthly precipitation climatology (Hou et al, 2014;Nelson et al, 2015). Conversely, 3B42 presents a very good agreement with GHCN-D (−1.7 %) and PRISM (−2.4 %) and contrasts with the severe underestimation observed on the right side of the distribution during winter (Fig.…”

“…Stage IV presents globally lower differences with GHCN-D (−14 to +1 %) and PRISM (−17 to +4 %) than the satellite estimates 3B42 (−28 to +7 %) and 3B42RT (−32 to +49 %). At the RFC level, Stage IV almost systematically underestimates precipitation except for two RFCs (AB, MB) with a lower rainfall of −7 % when compared to GHCN-D CONUS-wide (Table 2) the fact that the western RFCs use the Mountain Mapper approach and gauge-only estimates (Hou et al, 2014;.…”

“…Stage IV represents the final stage of the process that combines mosaicked estimates from the 12 RFCs. The gauges used at the RFC level for bias adjustment include available hourly rain gauges such as HADS (Hydrometeorological Automated Data System) gauges, ASOS (Automated Surface Observing System), and AWOS (Automated Airport Weather Stations) reports (Hou et al, 2014). Furthermore, some of the western RFCs (Colorado Basin: CBRFC; California-Nevada: CN-RFC; northwestern: NWRFC) do not use radar estimates due to poor coverage over mountainous areas.…”

Distributed Hydrologic Modeling Using Satellite-Derived Potential Evapotranspiration

“…Initial calibrated simulations with default-PET for the North Raccoon River, Redwood River, Blue Earth River, East Branch Pecatonica River, and Pecatonica River resulted in simulated discharge with 246.2%, 271.0%, 256.0%, 255.0%, and 272.7% PBias, respectively. Hou et al (2014) noted that CCPA precipitation estimates are better for lower and medium daily precipitation amounts compared to heavy precipitation events. Basin-averaged CCPA precipitation data are found to be consistently lower compared to mean areal precipitation (MAP) data obtained from the NCRFC for each study basin (Table 3).…”

“…CCPA is a 6-h precipitation product for the CONUS at the HRAP resolution available from 2002 to present. CCPA combines the high climatological reliability of the Climate Prediction Center (CPC) Unified Global Daily Gauge Analysis (24-h accumulation at 1 /88 resolution) and the high temporal and spatial resolution of the NCEP stage IV analysis (6-h accumulation at 4-km resolution) (Hou et al 2014).…”