Solid Precipitation Daqing Yang, Barry Goodison, Paul Joe, others ?? Role of snowfall Status of observations: gauge network, satellite, and radar Research examples Recommendations

1. Role of Solid Precipitation Significant portion of yearly precipitation in the cold regions (including the polar regions) – important indicator of climate change and variation Input to winter snowpack and spring snowmelt runoff in mountain and polar regions – critical element of basin water cycle and regional water resources Influence on large-scale land surface radiation and energy budget particularly during accumulation and melt seasons Effect on glacier/ice sheet accumulation/mass balance, lake/river and sea ice, seasonal frozen-ground and permafrost Impact to human society and activity, such as air/ground transportation, disaster prevention, agriculture, water resources management, and recreation…

Gauge network Global coverage with various operational, national/regional networks. Manual and automatic gauges, measuring water equivalent (amount), not snow particle size. Manual gauges can measure snowfall (rate) at 6-hour to daily time intervals, and auto gauges can provide hourly (or sub-hourly) snowfall (rate). Snow rulers are also used for snowfall observations at the national/regional networks, providing snow depth info, not SWE. Snow pillow/snowboard/snow depth sensor record snow accumulation changes over time - (in)direct info of snowfall. Gauge networks/data are long-term and fundamental, defining global snowfall/climate regimes and changes. 2. Status of Observations - gauges, satellite and radar

Satellites –Global coverage with merging data / products from IR, MV sensors and satellite radars –Rain rate info (TRMM), also snowfall rate ???, challenge with mixed precip –Particle size info from radars –Operational products - GPCP blended version 2 monthly/global, 1987-present, and others???? –Problems of MV data over land, need systematical evaluation particularly over the high latitudes –Limited validations show GPCP v2 data are not better than atmospheric reanalysis precip over northern regions (Serreze et al., 2005) –Statement of importance – Key to advance our capability of monitoring and observing (liquid/solid) precipitation globally???

Dataset Name (Reference) Spatial & Temporal Resol. and Coverage Data Sources and Merging MethodOnline Documentation CMORPH (Joyce et al. 2004) 0.25 o grid, 60°S - 60°N, 180°W - 180°E; 30 min., 12/2002-present Microwave estimates from the DMSP 13, 14 & 15 (SSM/I), the NOAA-15, 16 & 17 (AMSU-B) and the TRMM (TMI) satellites are propagated by motion vectors derived from geostationary satellite infrared data. cts/janowiak/cmorph_description.ht ml PERSIANN (Hsu et al. 1997) 0.25 o grid, 50°S - 50°N, 180°W - 180°E; 30 min., 3/2000-present A neural network, trained by TRMM TMI (2A12) precipitation, was used to estimate 30 min. precipitation from infrared images from global geosynchronous satellites. TRMM 3B42 (Huffman et al. 2005) 0.25 o grid, 50°S - 50°N, 180°W - 180°E; 3-hourly, 1/1998-present Microwave (TRMM, SSM/I, AMSR and AMSU) precipitation estimates were used to adjust IR estimates from geostationary IR observations. n/TRMM_README/TRMM_3B42 _readme.shtml Merged microwave only precipitation (X. Lin 2006, personal comm.) 2.5 o grid, up to 75°S - 75°N, 180°W - 180°E; hourly, 12/1997-present Estimates from TRMM TMI, SSM/I on DMSP F13, F14, F15, and AMSR-E from AQUA were first averaged on a 0.25 o grid and then further averaged to a 2.5 o grid. NCEP National Stage II multi-sensor hourly precipitation analysis ~4.8 km grid, continental U.S.; hourly, 5/1996-present About 140 WSR-88D radars over CONUS, and ~3,000 automated gauge reports were used in the analysis. /ylin/pcpanl/stage2/ – Operational products - GPCP blended version 2, monthly/2.5x2.5 grid, global, 1987-present Examples of RS Precip Dadasets

Summary Table: current/planned capabilities and requirements for space-based remote sensing of snowfall parameters (adopted from xxx, not done yet) C = Current CapabilityL = Low end of measurement rangeU = Unit T = Threshold Requirement (Minimum necessary)H = High end of measurement rangeV = Value O= Objective Requirement (Target) Parameter CTOCTO Measurement Range Measurement Accuracy Resolution Comment / Principal Driver SpatialTemporal LHUVUVUVU Snowfall amount C0100mm1 1km day MODIS/SSM I T0100mm0.25mm0.5km1day Hydromet O0100mm? 0.1km12hr Precip/Snowfall rateC0100mm/ hr 2-10cm25km1day AMSR- E/TRMM T0100mm/ hr 3cm0.5km6day Hydromet Transportatio n O0100mm/ hr 2cm0.1km12hr Precipitation typeCNon e --- Need HF SAR T %0.5km6day Hydromet O0.33 7%0.1km12hr Snow particle size C0~ km1daye.g. AMSR-E T km6dayHydromet O km1hr????

–Only cover very limited parts of the globe (much less extensive than the gauge network) –Expensive and can be difficult to operate and calibrate –Mainly designed for severe weather detection, with less concern for precipitation, certainly NOT for snowfall measurements, (although being used to measure snowfall with problems for light snowfall) –Major limitations for operational radars: lack of low level coverage at moderate (80 km) to long range for precipitation and this is even shorter for snowfall in complex terrain, the radar beam is often blocked by mountains and/or the radar is located to scan over the top of mountains and not in the valleys –A new innovation is the deployment of a network of redundant low cost, low maintenance radars (CASA radars) to scan the low levels of the atmosphere. –Statement of importance - key to understand cloud/precipitation physics and for validation of satellite precipitation data and products. Radar network

3. Research Examples gauge network and data RS snow data validation

Shortcomings in gauge network Sparseness of the precipitation observation networks in the cold regions. Uneven distribution of measurement sites, i.e. biased toward coastal and the low-elevation areas, less stations over mountains and oceans. Spatial and temporal discontinuities of precipitation measurements induced by changes in observation methods and by different observation techniques used across national borders. Biases in gauge measurements, such as wind-induced undercatch, wetting and evaporation losses, underestimate of trace and low amount of precipitation, and blowing snow into the gauges at high winds Data access is also difficult or costly for some regions and countries Decline of the networks in the northern regions/countries

Synoptic/climate stations on land above 45  N and the Arctic Ocean drifting stations

NRCS SNOTEL / Wyoming gauge network NRCS National Water and Climate Center

NOAA US CRN

National standard gauges tested in Barrow Canadian Nipher Hellmann Russian Tretyakov US 8”

Wind-induced gauge under-catch Wetting and evaporation losses Underestimate of trace precipitation events Blowing snow into gauges at high winds Uncertainties in auto gauge systems Biases in Gauge Meaurements (mentioned 3 times in IGOS Water Cycle Report)

WMO double fence intercomparison reference (DFIR) in Barrow, AK WMO Solid Precipitation Intercomparison CRN modified DFIR Goodison, B.E., P.Y.T. Louie, and D. Yang, 1998: WMO solid precipitation measurement intercomparison, final report, WMO/TD-No. 872, WMO, Geneva, 212pp.

Wind-induce undercatch: WMO intercomparison results

Overall mean for the NP drifting stations, (Yang, 1999) Overall mean for 61 climate stations in Siberia, (Yang and Ohata, 2001)

Precip (mm) Precip days Bias corrections of daily precipitation data, Barrow, (Yang et al., 1998)

a) Pm (mm)b) Pc (mm)c) CF Mean Gauge-Measured (Pm) and Bias-Corrected (Pc) Precipitation, and Correction Factor (CF) for January Total 4827 stations located north of 45N, with data records longer-than 15 years during Similar Pm and Pc patterns – corrections did not significantly change the spatial distribution. CF pattern is different from the Pm and Pc patterns, very high CF along the coasts of the Arctic Ocean. Yang et al., 2005, GRL

a) Pm (mm)b) Pc (mm)c) CF Mean Gauge-Measured (Pm) and Bias-Corrected (Pc) Precipitation, and Correction Factor (CF) for July Total 4802 stations with records longer-than 15 years during Similar Pm and Pc patterns. Small CF variation for rainfall over space. CF pattern is different from the Pm and Pc patterns. Yang et al., 2005, GRL

Jul. Jan. Impact of Bias-Corrections on Precip Trend Pm & Pc Trend Comparison, Selected Stations with Data > 25 Yrs during Yang et al., 2005, GRL

RS snow data validation - Comparison with in-situ snow data (scale issue) - Regional / basin water budget calculations to assess moisture budget closure:  Basin/region winter snow mass balance SWE = Snowfall – Sublimation  Basin spring water budget Runoff = SWE + Precip. – Evaporation – Storage - Hydrologic modeling and snow assimilation

Large Arctic rivers & their annual discharge to the Arctic Ocean/marginal seas 17% 9% 15% 5% 11%

Snow Water Equivalent (SWE) Information Streamflow inter- annual variation: Basin extreme (weekly- mean) discharge (m3/s). Data source: UNH/SHI

Snow Water Equivalent (SWE) Information 1.Lena basin has the highest winter snow pack, and Yenisei basin has the lowest. 2.The snow pack accumulate to the highest in winter, week For study convenience, when when SWE <0.5mm, the basin is considered ‘empty’. Basin SWE inter- annual variation Extreme (SSM/I) snowcover water equivalent (SWE, mm), Data source: NSIDC/UNH

Basin SWE (mm) vs. weekly discharge (m3/s), Lena R.,

Basin SWE vs. winter precip (mm), Lena R.,

Basin SWE vs. winter precip (mm), Ob R.,

4. Recommendations

Gauge networks and observations –Network continue conventional point precipitation measurements against declining networks in many countries sustain and enhance the gauge network in the cold regions; develop guidelines on the minimum station density required for climate research studies on solid precipitation in cold climate regions –Data undertake bias analysis and corrections of historical precipitation gauge data at regional to global scale ensure regular monitoring of the snowfall real-time data, quality control and transmission examine the impact of automation on precipitation measurement and related QA/QC challenges, including compatibility between national data, and manual vs. auto gauge observations develop digitized metadata for regional and national networks –Test facility/new technology identify and establish intercomparison sites for standardized testing of new technology, such as polarization radar, CASA radar networks, hot plate, pressure, or blowing snow sensors encourage national research agencies to establish programs to provide support for the development of new instruments to measure solid precipitation in high latitude regions use of wind shields and direct measurement of winds at emerging auto gauge sites/networks

–Need GPM ASAP and strongly encourage the EGPM mission to measure global rain/snowfall data, including major parts of the N regions –Need to blend (combine) data from different sources (in-situ, model, satellite) –Need to systematically evaluate RS snow data / products over cold regions via direct comparisons, analyses of basin water budget and compatibility in basin/region SWE-runoff, SWE-snowfall –Need to maintain reasonable expectations on what satellite and radar technologies are able to provide –Need for further intensive field efforts to address scaling issues. –Need for new technology development The use of combined active and passive satellite data for snowfall detection/retrieval should be further encouraged. Active space-borne instruments need to have a low detectability threshold (better than than 5 dBz) to detect light rainfall and snowfall. Deployment of rain radars with lower detectability threshold is encouraged. New passive microwave instruments and new channel combinations need to be studied, particularly at high frequency. The sounding channel technique proposed by the EGPM mission should be implemented. The new Meteosat Second Generation has many more channels than previous geostationary satellites. They have been able to provide information on particle size and phase. Exploration of these additional channels for precipitation estimation is encouraged. Aircraft sensors together with extended channel selection studies provide an excellent testbed for future satellite instruments. Dedicated high latitude aircraft campaigns for snowfall remote sensing are encouraged. Satellites

–Need to expand the radar networks to the northern/cold regions and to obtain more useful radar observations of snowfall. The CASA radar concept should be deployed with high sensitivity for the detection of snow, low level measurements and in complex terrain. –Need to share data and to create regional and global radar data sets international radar data quality intercomparisons to remove inter-radar biases of precipitation estimates. Availability of common or open source algorithms for generating precipitation estimates are needed to understand the biases and errors. –Need for development and further refinement of inexpensive ground- based remote sensing instruments for snowfall should be encouraged, including vertically pointing micro radars, such as (Precipitation Occurrence Sensing System) POSS or Micro-Rain-Radar (MRR). –Encourage use of combined active and passive satellite data for snowfall detection/retrieval –Need to study new passive microwave instruments and new channel combinations Ground Radar