Estimation of precipitation over the OLYMPEX domain during winter 2015-2016 Dennis P. Lettenmaier a, Qian Cao a, Tom Painter b, Jessica Lundquist c, Walter Petersen d a Department of Geography, University of California, Los Angeles, Los Angeles, CA b NASA Jet Propulsion Laboratory, Pasadena, CA c University of Washington, Seattle, WA d NASA Marshall Space Flight Center, Huntsville, AL OLYMPEX workshop March 22, 2017

Background and Objective A primary goal of Global Precipitation Mission (GPM) is to measure precipitation globally especially in areas lacking ground observations. One goal of the OLYMPEX campaign is to better assess precipitation products based on GPM and other satellites Especially in cold seasons and where orographic factors exert strong controls on precipitation Our objective To develop the best product we can for the evaluation of GPM-based precipitation products such as NASA’s IMERG over the OLYMPEX domain, which for our purposes was defined as the Olympic Peninsula plus the Chehalis River basin. Our period of analysis is winter 2015-16, which we define as Oct 2015 – Apr 2016.

Resources of data Precipitation data NOAA WSR-88D (primarily the site at Langley Hill, on the Washington Coast) NOAA’s National Severe Storms Laboratory (NSSL) Mountain Mapper product Precipitation gauges COOP (Cooperative Observer Network) CoCoRaHS (Community Collaborative Rain, Hail and Snow Network) SNOTEL RAWS (Remote Automatic Weather Stations) HADS (Hydrometeorological Automated Data System) ASOS (Automated Surface Observing System) OLYMPEX Snow data Snow depth maps for the interior of the Olympic Peninsula from two flights of NASA/JPL’s Airborne Snow Observatory (ASO) on Feb 8-9 and Mar 29-30 2016 In situ observations 4 SNOTEL sites

Langley Hill Radar Terrain blockage of the Langley Hill Radar coverage Due to terrain blockage, the radar captures precipitation on the west side of the mountain areas, but basically nothing on the east side

Precipitation gauges Map of precipitation gauges There are 120 rain gauges that were operational during at least 50% of the period Oct 1 2015 - Apr 30 2016: COOP 7 CoCoRaHS 77 SNOTEL 4 RAWS 1 ASOS 10 OLYMPEX 21 (in Quinault and Chehalis Basins) Very few stations are located at elevations higher than about 500m The distribution of gauges is nonuniform. Most gauges are on the east side while the coverage on the west is sparse

Sites at higher elevations Few stations are located at elevations higher than about 500m Much of the interior Olympic Mountain is above 500 m elevation with substantial winter snow cover, but few measurements Precipitation in this area is winter dominant a) yes there are 135 stations, but many big gaps b) much of the interior above ~500m elevation has substantial winter snow cover, but few measurements precipitation is winter dominate

ASO snow depth maps Feb 8 2016 snow depth map in 3 m resolution overlaid with 1/32 degree mesh Mar 29 2016 snow depth map in 3 m resolution 1/32 degree mesh for our product the February data extent is smaller because it was a partial collection due to weather etc

Methodology Estimation of precipitation at lower elevations Merge NOAA National Severe Storms Laboratory (NSSL) Radar product with Mountain Mapper Augment the merged product with 120 additional gauges Estimation of precipitation at higher elevations: We use VIC model driven by observed forcings and adjusted by ASO SWE ahead of two flight dates Temperature from SNOTEL and HOBO sites gridded by daily residuals subtracted from elevation-based seasonal mean Create SWE using ASO snow depth and density field generated by VIC and modified by site observations Adjust precipitation factor to force the simulated SWE to match the ASO SWE overall strategy for developing a precip product for the olympex winter gridded precipitation, temperature, and other surface variables available

Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model

Estimation of precipitation at lower elevations Merging NSSL MRMS radar and Mountain Mapper Radar Mountain Mapper Radar quality index mm mm

Estimation of precipitation at lower elevations Integration of MRMS radar precipitation with gauge observations Mean daily precipitation (Nov 1 2015~Mar 31 2016) MRMS merged Integrated with additional stations

Estimation of precipitation at lower elevations Evaluate merging method by systematically removing individual stations one at a time

Estimation of precipitation at higher elevations ASO snow depth aggregated to 1/32 degree

Estimation of precipitation at higher elevations ASO SWE Maps

Estimation of precipitation at higher elevations Mean daily precipitation (Nov 1 2015 – Feb 8 2016) Before Adjustment After Adjustment

Estimation of precipitation at higher elevations Mean daily precipitation (Feb 9 2016 – Mar 29 2016) Before Adjustment After Adjustment

Evaluation of IMERG IMERG (satellite only product) grids within the OLYMPEX domain Integrated Multi-satellitE Retrievals This algorithm is intended to intercalibrate, merge, and interpolate “all” satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for the TRMM and GPM eras over the entire globe. precpitationUncal: Multi-satellite precipitation estimate PrecipitationCal: precipitation estimates using gauge calibration over land randomError: random error estimate of precipitation Hqprecipitation: Instantaneous microwave-only precipitation estimate

Spatial comparison by month Integrated Multi-satellitE Retrievals This algorithm is intended to intercalibrate, merge, and interpolate “all” satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for the TRMM and GPM eras over the entire globe. precpitationUncal: Multi-satellite precipitation estimate PrecipitationCal: precipitation estimates using gauge calibration over land randomError: random error estimate of precipitation Hqprecipitation: Instantaneous microwave-only precipitation estimate

Storm interarrival time Comparison of exceedance probability of storm interarrival time (in hours) from October 1 2015 to April 30 2016 Ground obs includes hourly data from both radar and gauges

Daily comparison CDF of daily precipitation from October 1 2015 to April 30 2016

Seasonal comparison

Conclusions IMERG hourly data captures the temporal frequency of storms well except for Region II and IV(b) where radar and mountain mapper products show relatively low temporal correlation IMERG tends to underestimate precipitation for all winter months and over all sub-regions The underestimation is higher in mountainous region IV and is obvious especially for orographic enhancement in the mountainous interior of the OLYMPEX domain, up to 87% in region IV(a) on a seasonal basis IMERG shows a better match for the domain average in relatively inland Region II and III where there is less winter precipitation than coastal Region I with more precipitation On a monthly basis, IMERG shows smaller underestimation in October and April when temperature is higher and precipitation is less