Implementation of the NCEP operational GLDAS for the CFS land initialization
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1 Implementation of the NCEP operational GLDAS for the CFS land initialization Jesse Meng, Mickael Ek, Rongqian Yang NOAA/NCEP/EMC July
2 Improving the Global Land Surface Climatology via improved Global Land Data Assimilation System (GLDAS) NCEP operational GLDAS Upgraded Noah land model Higher-resolution land data sets, i.e. vegetation, soils, vegetation phenology (near-realtime), etc. Improved forcing, especially precipitation Land data assimilation (e.g. snow, soil moisture) Land model spin-up Including river routing to complete water cycle 2
3 3
4 Global Land Data Assimilation System (GLDAS) GLDAS (runs Noah land surface model (LSM) under NASA/Land Information System (LIS) forced with CFSv2/GDAS atmospheric data assimilation output & blended precipitation in a semi-coupled mode. Blended precipitation via satellite (CPC/CMAP; heaviest weight in tropics--satellite observations more accurate & surface gauges sparse), gauge (heaviest in mid-latitudes where gauge density highest) & GDAS (modeled; high latitude--gauges sparse, satellite obs lack accuracy). Snow cycled in CFSv2/GLDAS if model within 0.5x to 2.0x observed value (IMS snow cover & AFWA snow depth products), else adjusted to 0.5 or 2.0 of observed value. GDAS CMAP precip Gauge locations IMS snow cover AFWA snow depth 4
5 NCEP Realtime Operational GLDAS/LIS Land Surface Characteristics Topography Land Cover Soil Precipitation Forcing Non-precip Meteorological Forcing Land Variables Soil Moisture Soil Temperature Snow Noah LSM Land Information System Christa Peters-Lidard et al., NASA/GSFC/HSB CFS/GFS land analysis Soil Moisture Soil Temperature Snow CFS/GFS land initial conditions gdas1.t00z.sfcanl 5
6 GLDAS Global Land Surface Climatology Motivation: CFSR was executed in 6 streams, discontinuity at stream boundaries. Solution: One stream GLDAS (1979 realtime). Configuration: Same as CFSR (LIS T382). Forcing: CFSR surface forcing and blended (obs+model) precip forcing. Initial condition: Spin up land states for 1 January, Spin up: 15 years (5 repeating year of 2003; followed by 10 repeating year of 1979) 6
7 NCEP Realtime Operational GLDAS CDAS/GDAS Atmospheric analysis Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 GLDAS Land analysis GLDAS Land analysis Precip observation Precip observation 7
8 GLDAS soil moisture climatology 8
9 GLDAS soil moisture anomaly 9
10 GLDAS soil moisture anomaly Russian drought Uganda flood US drought Global Drought (Flood) Monitor 10
11 NLDAS Support for NCEP/CPC Drought Monitoring and Assessment Activity 11
12 Drought is a natural hazard around the world. Expand the current NLDAS drought monitor and prediction applications to global is desired. 12
13 Vegetation Types: SIB vs IGBP 1: broadleaf evergreen trees! 2: broadleaf deciduous trees 3: broadleaf and needleleaf trees! 4: needleleaf evergreen trees 5: needleleaf deciduous trees (larch) 6: broadleaf trees with groundcover 7: groundcover only (perennial) 8: broadleaf shrubs with perennial groundcover 9: broadleaf shrubs with bare soil 10: dwarf trees and shrubs with groundcover (tundra) 11: bare soil 12: cultivations (the same parameters as for type 7) 13: glacial ice 1:Evergreen Needleleaf Forest 2:Evergreen Broadleaf Forest 3:Deciduous Needleleaf Forest 4:Deciduous Broadleaf Forest 5:Mixed Forests 6:Closed Shrublands 7:Open Shrublands 8:Woody Savannas 9:Savannas 10:Grasslands 11:Permanent wetlands 12:Croplands 13:Urban and Built Up 14:Cropland/natural vegetation mosaic 15:Snow and Ice 16:Barren or Sparsely Vegetated 17:Water 18:Wooded Tundra 19:Mixed Tundra 20:Bare Ground Tundra 13
14 Soil Types: ZOBLER vs STASGO 1 loamy sand 2 silty clay loam 3 light clay 4 sandy loam 5 sandy clay 6 clay loam 7 sandy clay loam 8 loam 9 loamy sand 1: sand 2: loamy sand 3: sandy loam 4: silt loam 5: silt 6:loam 7:sandy clay loam 8:silty clay loam 9:clay loam 10:sandy clay 11: silty clay 12: clay 13: organic material 14: water 15: bedrock 16: other (land ice) 17: playa 18: lava 19: white sand 14
15 A Gauge-Satellite Blended Analysis of Daily Precipitation for Hydrometeorological Applications 0.25 o lat/lon over the global land Daily analysis from 1979 Blending information from different sources to overcome shortcomings CPC daily gauge analysis adjusted to GPCC monthly gauge data to correct the under-estimation in daily reports OLR-based precipitation estimates derived through calibration against CMORPH Daily gauge data and OLR precip combined to produce a precipitation analysis with long-term homogeneity and quantitative accuracy Right figure: sample for Jul. 15,
16 DATA ASSIMILATION: SNOW An improved land data assimilation component in GLDAS will decrease errors and improve the quality of the reanalysis. This would be a first effort that targets incorporating extensive satellite observations of land data sets into a NOAA/NCEP operational global reanalysis. MODIS Snow Cover DA SMMR SWE DA Comparison of snow water equivalent between the open loop simulation (green), the assimilation simulation (red) and the in situ measurement (black) averaged over all SNOTEL sites in the study region. Comparison of the median SWE for pixels including 5 or more stations; ground observations (black dots), SMMR observations (plus), model forecast (dash lines), model forecast with assimilation run I (dotted lines) and run II (solid lines) from (a) January to March in 1979 and (b) from July 1986 to June 1987 (zoomed to the winter months from October 1986 to April 1987). 16
17 SPINUP STRATEGY SPINUP: 1986 SPINUP: 1985 Precipitation is a major factor affecting spinup time. Regions with annual precipitation over 1000m (NE and SE) requires spinup time less than 1 year. Regions with annual precipitation between 500mm and 1000mm (CE and NW) require the 4 5 years spin up time. Regions with annual precipitation less than 500mm (NC and SW) requires nearly 10 year spinup time. Temperature affects model spinup time in dry regions. SPINUP: 1985&1986 SPINUP: 1984&1985 CFSR ran with six simultaneous streams (separate runs) over the 32 year period ( ) with the issue of discontinuity of evolving land states. A sufficient spin up process for each single stream is required to maintain the continuity of land states. 17
18 Streamflow from NLDAS routing scheme Ensemble mean daily streamflow anomaly (m 3 /s) Hurricane Irene and Tropical Storm Lee 20 August 17 September
19 Streamflow from NLDAS routing scheme Ensemble mean daily streamflow anomaly (m 3 /s) Superstorm Sandy 29 October 04 November
20 Summary Improved GLDAS Upgraded Noah land model Higher-resolution land data sets, i.e. vegetation, soils, vegetation phenology (near-realtime), etc. Improved forcing, especially precipitation Land data assimilation (e.g. snow, soil moisture) Land model spin-up Include river routing to complete water cycle Land surface initial states (ensemble) for S2S prediction experiments 20
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