1 / 31

Improving Soil Moisture Monitoring and Prediction for Land Surface Variables in Kunming

Enhancing soil moisture data for drought and flood monitoring, validating land surface model through intercomparisons, offering consistent initial conditions for forecasts, and developing a long-term dataset for predictability studies in Kunming.

russellv
Download Presentation

Improving Soil Moisture Monitoring and Prediction for Land Surface Variables in Kunming

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Kunming, May, 2004 NWS-CPC's Monitoring & Prediction of Soil Moisture & Associated Land Surface Variables Yun Fan, Huug van den Dool, Dag Lohmann, Ken Mitchell CPC/EMC/NCEP/NWS/NOAA

  2. Motivations • Improve soil moisture data for CPC’s drought & flood monitoring tools • Improve land surface model via data validation and model intercomparisons • Provide model consistent initial conditions for various ranging forecasts • Provide a long time series of realistic land surface data for land memory & predictablity studies • Coupled atmosphere-land-ocean modeling • Other……

  3. CPC Leaky Bucket Soil Moisture Model(Huang et al 1996) The soil moisture budget over an area A: Where W(t) is soil water content P(t) precipitation E(t) evaportranspiration R(t) net streamflow divergence G(t) net groundwater loss Forcing Data: -CPC daily temperature updates -CPC daily precipitation updates (Higgins & Shi) -Monthly precipitation and temperature from NCDC

  4. CPC Leaky Bucket Soil Moisture Model- cont Output Data Coverage: - 72 years (1931-yesterday) on 344 US climate divisions - 56+ years (1948-present) on global domain Products Web Site (daily & monthly updated): -http://www.cpc.ncep.noaa.gov/soilmst/ (official site) -http://www.cpc.ncep.noaa.gov/soilmst/index.htm(test site)

  5. Current CPC soil moisture related monitoring & predictive activities: • Drought & flood monitoring • Empirical forecast tools (Constructed Analog) • GFS forecast & climate prediction

  6. CPC Leaky Bucket Model - Monitoring

  7. CPC Leaky Bucket Model - GFS bias corrected ensemble forecasts (daily Prcp and Temp at 0Z) are used to drive the soil moisture model.

  8. CPC Leaky Bucket ModelCA Monthly and Seasonal Forecasts

  9. Retrospective LDAS Run ProjectA joint project to Land Data Assimilation System (LDAS) Project • NOAH Land Surface Model • Physically far more complete • Higher resolution (spatial: 1/8 degree grid, temporal: hourly) • Forcing Data (1948-1998): • Observed precip (Higgins & Shi) • Atmospheric forcing (From global reanalysis) • Outputs Will Provide: • Improved soil moisture & associated land data variables. • Superior model consistent initial conditions • others

  10. Data validation cr=0.70 cr=0.73 cr=0.55

  11. Jan &Jul Climatology (1961-1990) of all water balance components

  12. Land Surface Water Budget Over Conterminous USA

  13. US monthly values of all components of land surface hydrology (mm/mon) Mon          W      P            E         R+G     P-E-R-G 1      581.2 (294.2)     52.0       5.5     31.8       14.6 2      592.5 (304.0)    49.8      10.3      33.3        6.2 3      600.0 (308.8)      62.9      24.5      39.1        -0.7 4      595.6 (302.0)      59.4      42.8      30.2      -13.7 5      582.8 (289.8)    70.9      64.8       21.2      -15.1 6      564.6 (276.8)     66.2      75.7      14.2      -23.8 7      538.4 (260.1)     64.8     77.8       10.4      -23.5 8      520.1 (250.0)      62.2      67.6        9.0      -14.3 9      514.3 (248.3)     61.4      49.3        8.8         3.3 10     520.2 (253.3)      51.7       31.9        9.1     10.8 11     539.8 (266.8)    58.0      14.0      13.5       30.5 12     565.8 (282.6)     58.5       4.5      27.3       26.8 Year  559.0 (278.1)     59.8     39.1      20.7        0.0 averaged over 125W-75W, 30N-48N

  14. P(t) - E(t) - R(t) – G(t)

  15. Simulated extreme hydrologic events: 1988 drought & 1993 flood Simulated extreme hydrologic events: 1988 drought & 1993 flood

  16. Summary & Future Work • Monitoring & predicting land surface variables • LSMs can simulate some realistic land features • Need more detailed analysis & studies • Data validation, model comparisons & improvements • Modeling & Prediction experiments • Global Retrospective LDAS Run • Others

More Related