MesoWest: One Approach to Integrate Surface Mesonets

Transcription

1 : One Approach to Integrate Surface Mesonets John Horel Chris Galli Brian Olsen Judy Pechmann Department of Meteorology University of Utah ASOS RAWS Background A cooperative program to collect, archive, and distribute environmental observations across the Nation with emphasis on the western United States 200+ agencies/commercial firms 1000s of HAM radio operators stations nationally (3500+ stations in western US) Primary support: NWS and BLM Considerable effort placed on basic metadata and MySQL database Delivery of data via FTP,LDM, web portals Traditional COOP reports in separate database Integration of environmental and GIS information SNOTEL OTHER began over ten years ago to integrate the collection, archival, and distribution of weather observations available from hundreds of sources Approx temperature obs/hr across the West ingested into ADAS is used extensively for operational, research, and educational use with specific applications developed for fire weather operations Extensive coordination with Meteorological Assimilation Data Ingest System (MADIS) staff to manage metadata, acquire new data sources, and deliver data to government and other users Metadata provided by station owners is integrated with GIS information to georeference objectively the data relative to: states and counties National Weather Service (NWS) County Warning Areas, forecast zones, and fire weather zones Land agency Geographic Coordinating Areas and Predictive Service Areas Locations of fires Topozone and google earth graphics available for every station 1

2 Limitations of ObservationsAll That Is Labeled Data Is NOT Gold (Lockhart 2003) References: Challenges of Measurements. T. Lockhart (2003). Handbook of Weather, Climate and Water. Wiley & Sons Review of the RAWS Network. Zachariassen et al. (2003). USDA Tech. Report RMRS-GTR-119. Are All Observations Equally Good? Are All Observations Equally Bad? All measurements have errors (random and systematic) Errors arise from many factors: Siting (obstacles, surface characteristics) Exposure to environmental conditions (e.g., temperature sensor heating/cooling by radiation, conduction or reflection) Sampling strategies Maintenance standards Metadata errors (incorrect location, elevation) Why was the sensor installed? Station siting results from pragmatic tradeoffs: power, communication, obstacles, access Use common sense Observing needs and sampling strategies vary (air quality, fire weather, road weather) Wind sensor in the base of a mountain pass will likely blow from only two directions Errors depend upon conditions (e.g., temperature spikes common with calm winds) Use available metadata Topography Land use, soil, and vegetation type Photos SNZ Monitor quality control information Basic consistency checks Comparison to other stations UT9 GNI How representative can a single observation site be? Adequate instrumentation Good local siting Sub-NDFD Grid Scale (5km) Variability in Terrain Height Density of Temperature Observations (A σ z /#) Dark > 200m Response to synoptic conditions can vary widely over short distances 9-1 Persistent ridging can lead to cold pools in basins and warm temperature on slopes Response dependent on snow cover as well Myrick and Horel (2006) METAR For σ z = 200m Green: 1 stn every 50x50km2; Light red: 1 stn every 35x35 km2; Red: 1 stn every 25x25 km 2 2

3 ROMAN and : resources for observing surface weather ROMAN Goals and ROMAN are software that require ongoing maintenance and development to provide access to surface environmental information for variety of operational applications Shoestring budget! Maintain software that accesses RAWS data in ASCADS and make that data available for operational users in real time Integrate RAWS, ASOS, and mesonet observations into one archival and display system to provide real-time weather data around the nation to meteorologists and land managers Display data in fast-loading formats tailored to the wildland fire community and accessible to: How Mesonet Data Are Accessed and Delivered Data Streams Preprocessing Top-level managers Fire-behavior analysts and IMETs in the field NWS WFO operations RAWS In ASCADS San Diego Tribune. 28 Oct What Weather Information is Available? Search by: maps (state, CWAs, GACCs, etc.) San Diego Tribune. 28 Oct What Weather Information is Available? Search by: zip code, geographic location, latitude/longitude Other WR/WFO Apps Web UU ROMAN Web Server Users Metadata/QC UU What Has Been Happening Recently? 5-Day Max/Min Temperature, RH, Wind Speed 3

4 What Are the Current Conditions? Weather Summary What Has Changed Since Yesterday? Trend Monitor Current Weather Near Fires How Much Precipitation Has Fallen? Monitor What Extreme Conditions Are Underway? Weather Monitor Summary Weather Near Fires 4

5 Archived Fires Weather Near Fires: 31 October 2003 MODIS Base Maps October 31 October 29 Quality Checking The Mix of Observing Assets John Horel Mountain Meteorology Group Department of Meteorology University of Utah utah utah.. edu Google API User Interface 5

6 ADAS Data Flow Near-real time surface analysis of T, RH, V (Lazarus et al WAF; Myrick et al WAF; Myrick & Horel 2006 WAF WAF)) Data Streams Analyses on NWS GFE grid at 5 km spacing Metadata/QC/ UU Background field: RUC Horizontal, vertical & anisotropic weighting Preprocessing RAWS In Quality Checking MADIS/LDM Delivery Designed to identify quickly provisional observations that have egregious problems or are inconsistent with surrounding observations One QC flag reported for each observation (not specific variable) Other WR/WFO Apps Suspect (Red/-1): fails simple gross checks Unknown (0): QC flag not available Caution (Orange/1): significant departure from 3D multivariate linear regression estimate or fails wind persistence check OK (Green/2): passes all checks Web UU ROMAN Web Server See Splitt et al. (WMO 2001), Horel et al. (BAMS 2002) Focus on temperature, wind, pressure, and relative humidity Current automated QC is crude Current manual QC is cumbersome Designed to identify stations with consistently poor observations prior to use in ADAS data assimilation (ADAS blacklist file) One QC flag reported for each station manually (not specific observation) Uncertainty regarding metadata Manual identification of problem with 1 or more variables over extended period of time Stations that frequently report observations that differ extremely from ADAS are flagged 3D Regression Check RUC/ADAS Temp. Analysis vs. Observations 6

7 Qv (specific humidity) Wind Speed Probability of 1-hour Temp Change (F) at UT16 ( ) -21.6F in 1h 15.1F in1h Quality Checking Issues Applications of RTMA as background field Hourly analysis at 5 km resolution available 30 minutes past hour (T,RH,V,precip,cloud) Taking full advantage of nearby observations Representativeness of nearby observations for buddy checking Impacts of differences in elevation, terrain blocking, etc. on Barnes analysis 7

8 Random Thoughts RTMA NCEP Real Time Mesoscale Analysis in development & testing 2DVar approach with RUC as background Integrating Observing Assets What We Do Well Well QC Information along with original data must be accessible to the end user Incomplete metadata affects application of QC algorithms (low resolution lat/lon, incorrect elevation) Flexibility required as new mesonet stations added frequently QC work underway to: improve automated methods (including use of Kalman Filter) Use climatological approaches as 10-year database for some stations integrate more completely QC from ADAS into use MADIS and other (Clarus?) QC sources Google/Open Layers API Display Environment Interactive displays: 5 km temperature analysis from NCEP Real Time Mesoscale Analysis (RTMA) and temperature observations 8

9 NCEP RTMA Precipitation Analysis and observations What We Struggle With Real-Time Quality Checking Splitt et al. (WMO 2001), Horel et al. (BAMS 2002) Focus on temperature, wind, pressure, and relative humidity Current automated QC is crude Designed to identify provisional observations that have egregious problems or are inconsistent with surrounding observations One QC flag reported for each observation (not specific variable) Current manual QC is cumbersome Designed to identify stations with consistently poor observations prior to use in data assimilation Stations that frequently report observations that differ extremely from ADAS are flagged Real-Time Precipitation Data Hardest to manage due to differences in Equipment and measurement technique Measurement type (interval, sum) Reporting interval (5 min-24 hour) Hardest to quality control Unheated tipping buckets Representativeness issues Difficult to integrate QC procedures developed for hydrologic applications (e.g., 24-h total QC d data from NRCS) into real-time data stream 9