Presentation on theme: "Implementation and test of HRLDAS in CWB"— Presentation transcript:
1Implementation and test of HRLDAS in CWB VV Tsay, Hong, Jing-Shan
2Outline Concept of HRLDAS Implementation and education Test of HRLDAS at CWBFuture plan
3Statistical relations between the grid and observations In atmosphere, most observations were distributed within 3-D model grid. In usual, information is needed when one try to interpolate the observation to model grid:Firstguess, that means somehow, you trust the model atmosphere governed by the fluid dynamic and physical processStatistical relations between the grid and observations
4In land, the situation is quit different, In usual, the observation in the land is very limited.In relative, one can find much more observation on the ground level, -- the boundary of the land model.We can use the atmospheric forcing near the surface, and trust the land process in the land model, to initialize the state of the land.The information of the surface forcing will propagate to the deep layer in a certain time.This boundary is most active for the energy and moisture exchange between the land and atmosphere.
5The first approach for the offline HRLDAS integration in CWB Ingest the hourly atmospheric forcing:- RAINRATE from CMORPH (satellite retrieval)- PSFC, T2D, Q2D, U2D, V2D (GDAS analysis)- LWDOWN, SWDOWN, WEASD (GLDAS)- VEGFRA (WPS)
6The second approach for the offline HRLDAS integration Ingest the hourly atmospheric forcing:- RAINRATE from CMORPH (satellite retrieval) and Taiwan local precipitation data from QPESUMS- PSFC, T2D, Q2D, U2D, V2D (WRF forecast)- LWDOWN, SWDOWN(WRF forecast)VEGFRA (WPS)WEASD (GDAS)
7Summary of the implementation NCAR provide an one-month dataset and tutorial. CWB and NCAR perform the same integration using the same dataset and do the inter-comparison in Apr. (NCAR/CWB)Provide a driver to ingest the WRF forecast as the atmospheric forcing in HRLDAS (NCAR)Perform a 5-days integration and to ensure CWB and NCAR have the same results. (NCAR/CWB)Perform a series of the forecast experiment to evaluate the HRLDAS performance.
8Data used in the forecast experiment driven by GDAS (NCAR) Collect the analysis field from GDAS from Jan-MarExtract PSFC, T2D, Q2D, U2D, V2D and interpolate to 1-hr intervalCollect the analysis field from GLDAS from Jan-MarExtract LWDOWN, SWDOWN, WEASD and interpolate to 1-hr intervalPrepare the 3-month CMORPH observations and interpolate to 1-hr interval.Prepare the VEGFRA from WPS
9Data used in the forecast experiment driven by WRF forecast (CWB) Re-run the WRF model to extract the hourly forecast output from Jan to June1-6 hr, 7-12 hr, and hr forecastExtract the PSFC, T2D, Q2D, U2D, V2D, LWDOWN, SWDOWN, from WRF forecast.VEGFRA from WPSIt is difficult to find the observational and analysis LWDOWN and SWDOWN in realtime.To further understand the impact of the WRF output in different forecast period on the HRLDASPrepare the WEASD from GDASPrepare the 6-month CMORPH observations and interpolate to 1-hr interval.Prepare the 6-month hourly radar estimated rainfall from QPESUMS.
10Experimental design Exp D1-C (CWB) Exp D1-CA (NCAR) Exp D3-C (CWB) 45-km resolution, 6-month integrationUse 7-12-hr WRF forecast as forcingCMORPH rainfallExp D1-CA (NCAR)45-km resolution, 3-month integrationUse the analysis from GDA and GLDAS as forcingExp D3-C (CWB)5-km resolution, 6-month integrationExp D3-Q (CWB)CMORPH rainfall + QPESUMS rainfallWRF (CWB)Land variables in the original couple WRF-NOAH, interpolated from the land model of CWBGFS
34SummaryCases show that the HRLDAS driven by the WRF forecast running in a reasonable way.Comparison among D1C, D1CG, and WRFThe amplitude of the diurnal cycle decrease as the depth increased.In 45-km resolution,the ground temperature in D1-C is colder from the surface to 2nd layer, but warmer in 3rd and 4th layer than those in operational WRF-NOAH coupled model.The soil moisture in D1-C is drier than WRF-NOAH, however, the soil is near saturate in WRF-NOAH, it looks questionable
35SummaryThe ground temperature is warmest in WRF, the D3-C the second, and the D3-Q the coldest.The soil moisture in D3-Q is wetter than the other 2 runs.Deep layer soil is near saturate in D3-Q, it looks consistent with the heavy rainfall over south Taiwan in Meiyu season?
36Future planThe test to ingest WRF forecast at 1-6-hr and hr is undergoing, to show the sensitivity of WRF forecast.If not sensitivity, that’s good news.If very sensitivity, that we have to choice the atmospheric forcing very carefully.Can we find the realtime radiation products?Is the HRLDAS reach the equilibrium state in 6-mon integration?The more evaluation about the integration results.How to do?
37Plan in 2009 Improvement of the soil texture over Taiwan island. Improve the vegetation type from the MODIS productsEnhance the verification of the HRLDAS using flux data and land in-situ dataTesting of the coupled HRLDAS-WRFTraining about the use of satellite products, advanced education on land process, and coupling the WRF and HRLDAS.