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Operational Monitoring and Prediction Products in support of NIDIS

This project focuses on monitoring atmospheric and hydrological conditions to support the operational Drought Monitor, providing users with timely information and analysis for drought management. Accomplishments include an automated Drought Classification System and real-time monitoring of national drought conditions. Meteorological, agricultural, and hydrological drought aspects are tracked through standardized indices. Forecast products are verified regularly to enhance operational drought outlook. Plans include improving P analysis resolution and incorporating satellite-derived products.

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Operational Monitoring and Prediction Products in support of NIDIS

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  1. Operational Monitoring and Prediction Products in support of NIDIS Kingtse Mo & Wanru Wu Climate Prediction Center

  2. Our Goals • Monitor atmospheric and hydrological conditions in support of operational Drought Monitor • Provide users timely information and analysis on drought. • Develop regional applications in support of the NIDIS pilot project • Verify forecast products in support of operational drought outlook

  3. Accomplishments • Automated, real-time objectiveDrought Classification System based on drought indices. • Real-time monitoring and attribution of National drought conditions based on the North American Regional Reanalysis (atmosphere) and NLDAS (hydrology) for the NIDIS portal. • Drought briefing and teleconference to provide information to users, the NWS regional offices and the RFCs. Thank CPPA for support

  4. Operational Drought Briefing and Teleconference: (http://www.cpc.ncep.noaa.gov/products/Drought) • Drought briefing and teleconference: • Real-time monitoring and objective forecast products • Second Thursday each month • Coordinated with production of operational Drought Monitor and Seasonal Drought Outlook

  5. Objective MonitoringMany Aspects of drought • Meteorological drought: Standardized precipitation indices (SPI): 3, 6 ,12 and 24 months based on P • Agricultural drought : • Soil moisture percentiles (ensemble NLDAS) • Hydrological drought: • Standardized runoff indices (SRI): 3, 6, 12 and 24 months

  6. Monitoring Data sources • Atmospheric conditions: NARR (produced by the CPC, 1979-present) • Hydrological conditions: Ensemble NLDAS (SAC,Noah and Mosaic, VIC) produced by the EMC Ken Mitchell’s group 1979-present • Precipitation : the CPC operational P analysis 1948-present • Satellite: Green Vegetation Fraction –NESDIS

  7. June 2008 AMJ 2008 Example: Jun 2008 • Heavy flooding over Iowa, Mo,& Illinois, Kansas in June • Dryness over the Southeast & Texas and California • This pattern persisted from April Kansas : western Kansas : D3 drought Eastern Kansas: Above normal rainfall

  8. Meteorological drought 1. Rainfall over the Mississippi basin influenced all SPI indices. 2. Dry: Southeast, southern Texas and California 3. Wet: Central U. S. and the upper Missouri basin (RFC 2 area)

  9. Agricultural drought University of Washington ensemble: Base: 1920-2003 Base:1979-2006 Ref: Mo 2008 They all capture basic features: Drought: SE, southern Texas and California Wetness: great Plains, but details differ

  10. Hydrological droughtStandardized Runoff index The standardized indeces from 3 models are similar. They all capture Wetness over the Mississippi, Upper Missouri , Dryness over the Southeast, Texas and California Compare well with USGS streamflow

  11. Gaps • P analysis needed to improve • Soil moisture and surface properties are taken from the NLDAS. They depend on model, input data and record length. • Drought can be local. The NLDAS has 1/8 degrees resolution, but the uncertainties are too large to resolve regional features. • Forecasts: U. Washington (ESP), Princeton U. (VIC model driven by CFS outputs), NSIPP (only soil moisture). Plans • 1. The CPC unified gauge P analysis from 1950-present will be ready in 2009. Resolution 0.125o over the U. S. We will use that. • 2. We need independent indices not based on models. For example: Satellite derived products. • 3. Spread will be provided once we have VIC outputs

  12. April Monthly mean Satellite derived ESI anomalies for 2002 based on the ALEXI model May NLDAS ensemble June July August Sept ESI=1- E/PE Ref: Anderson et al. 2008

  13. Downscaling: RSM (CTB project)

  14. What do we need? • More satellite products : verify and determine the relationships with objective indices based on the ensemble NLDAS products. • Build a bridge to NWS regional offices and RFCs: Work with CBRFC to develop applications in support of the NIDIS pilot study: Colorado River basin • We have forecast products from different places. They need to be verified uniformly and establish regional skills

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