Application of Satellite Land Surface Observations in Improving NCEP Numerical Weather Prediction

Weizhong Zheng1,2 and Mike Ek1

1NOAA/NCEP/Environmental Modeling Center , College Park, MD

2IMSG @NOAA/NCEP/EMC, College Park, MD

Cooperators: J. Meng, H. Wei, J. Dong, Y. Wu, R. Sun, J. Han and Global Branch (NCEP/EMC); GSI/CRTM teams; X. Zhan's team, F. Weng's team and M. Vargas's team (NESDIS); X. Zeng (UA); C. Peters-Lidard (NASA); J. Eylander (Air Force); etc.

The 13th JCSDA Technical Review Meeting & Science Workshop, May 13-15, 2015

ClimateCFS

Motivation

➢ Objective: To improve satellite data utilization over land in NCEP data assimilation system and then improve the numerical weather prediction (NWP).

➢Land satellite data assimilation: – Utilization of satellite data sets in the models (e.g., GVF, snow, burning area, albedo, radiation, vegetation and soil type) – Assimilation of satellite products (e.g, Soil moisture: AMSR2 data; snow); – Direct radiance assimilation (Tb) (sfc parameters, sfc emissivity and sub-grid scale land surface)

➢ Land-Atmosphere Interaction – Identify and understand the interaction and feedback between land and atmosphere, and then improve NWP and DA.

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Status of Weekly Real-Time VIIRS GVF

➢ Objective: Use weekly real-time VIIRS GVF data set in the NCEP models to replace the old 5-year mean monthly climatology from AVHRR.

➢ Last year: Summer case had been tested in GFS.

➢ Current status: More cases and impacts are continuously investigated, included in NAM.

➢ Main achievements from last year. – New GVF showed a good improvement for NWP.

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CPC Soil Moisture Anomaly

Courtesy CPC Monthly Climate Review

GVF: 15 May 2014 Drought in Spring

Clim

VIIRS

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Surface temperature and its RMSE CONUS East

Reduced cold bias(~0.5 °C) and RMSE (~0.25 °C) afternoon and nighttime

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Surface temperature and its RMSE CONUS West

Reduced cold bias(~1 °C) and RMSE (~0.25 °C) afternoon and nighttime, but increase a little daytime RMSE.

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Surface dew point temp and its RMSE CONUS East

Reduced wet bias and RMSE afternoon and nighttime (~0.5 °C)

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++

0+

++++

0+++ --

Forecast Verification Statistics (FVS) regions

+: improve; -: degrade; 0: neutral

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Precipitation Skill Scores over CONUS: f60-f84

Improved scores for light and medium precipitation. 9

GVF Testing in NAM

Climatology

VIIRS-Snow

VIRRS-No-Snow

Clima

VIIRS-Snow

VIIRS-No Snow

➢ 5 year climatology of GVF currently used in NAM

➢ Near real time VIIRS GVF corrected with phenology prediction and snow contamination

➢ Near real time VIIRS GVF corrected with phenology prediction and without snow contamination

➢ Near real time VIIRS GVF from Marco’s Group

➢ NAM will be run for 4 different seasons in 2014 to compare the effect of different GVF product in NAM forecast

Courtesy Yihua Wu

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➢ Surface physical characteristics in NAM are updated based burning products derived from satellite

➢ Two burned files are read in by the surface update program of NAM:

-- 30 day accumulation of burned area – 2 day accumulation of burned area which is included in the 30 day accumulation

➢ Vegetation cover, roughness and albedo are updated based on the 30 day accumulation while the surface temperature, soil temperature and moisture of the top soil layer are updated based on the 2 day accumulation

➢ Two Fire cases (the Wallow Fire in 2011 and the Carlton Complex fire in 2014) were tested. Courtesy Yihua Wu

To Incorporate Fire Effect in NAM

GVF

ALB

Zo

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Status of SMOPS Soil Moisture Assimilation

➢ Objective: Assimilate SMOPS SM products and improve NWP.

➢ Last year: Some cases tested; SMOPS re-scaled with GDAS.

➢ Current status: More validation using more in situ SM measurements; New SMOPS SM product test.

➢ Main achievements from last year. – Validation indicated that SMOPS blended SM quality is impacted

by low quality SM retrievals from WindSat. – Soil moisture retrievals from AMSR2 is expected to improve

SMOPS blended soil moisture when NESDIS-GCOM-W system becomes operational..

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SMOPS0.39

ALEXI0.44

WindSat0.28

ECV0.47

Comparison with in Situ Measurements (2007-2013)

ECV: Essential Climate Variable data set

ALEXI: Atmosphere-Land Exchange Inversion (ALEXI) model using thermal infrared satellite observations of LST.

Courtesy X. Zhan et al 13

Test Result of SMOPS Soil Moisture including AMSR2

AMSR2

ASCAT

SMOS

SMOPS

Courtesy X. Zhan et al 14

Satellite-based Land Data Assimilation Tests in NWS GFS/CFS Operational Systems PI: Michael Ek (NOAA/NCEP/EMC)

Co-Is: Jiarui Dong and Weizhong Zheng (IMSG at NOAA/NCEP/EMC) Christa Peters-Lidard (NASA/GSFC) and Grey Nearing (SAIC at NASA/GSFC)

We propose to enable the existing NASA Land Information System (LIS) to serve as a global Land Data Assimilation System (LDAS) for both GFS and CFS. LIS integrates NOAA/NCEP’s operational land surface model (NCEP’s Noah), high-resolution satellite and observational data, and land data assimilation (DA) tools.

The LIS EnKF-based land Data Assimilation tool is used to assimilate soil moisture from the NESDIS global Soil Moisture Operational Product System (SMOPS), snow cover area (SCA) from operational NESDIS Interactive Multisensor Snow and Ice Mapping System (IMS) and AFWA snow depth (SNODEP) products.

Courtesy Jiarui Dong15

Status of Land-Atmosphere Interaction

➢ Objective: Identify and understand the interaction and feedback between land and atmosphere, and then improve NWP and DA.

➢ Last year: Continuously investigated the cause of surface temperature biases in the NCEP models which affect DA too.

➢ Current status: Together with field exp., improve physical schemes.

➢ Main achievements: – GFS late afternoon rapidly cooling identified and fixed – Improvement of GFS surface temperatures forecast. – Improvement of GFS precipitation forecast.

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Why is important to study land-atmosphere interaction for data assimilation (DA)

A.Direct radiance assimilation: Requiring a forward radiative transfer model (RTM) to

calculate Tb with input of model atmospheric profiles and surface parameters.

For surface-sensitive channels, Tb simulation largely

depends on: (a) Sfc parameters such as LST and soil moisture; (b) Sfc emissivity (IR/MW) Thus, in order to improve radiance DA, we have to improve

sfc emissivity calculation and sfc parameters simulation. Our efforts on land DA must include these two tasks.

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Courtesy G. Manikin

Comparison of T2m

(F): NAM, GFS and Obs, 00UTC, 2015-02-17

NAM

GFS Obs

CTL: Rapidly cooling up to 15 °C for 3h;EXP: Substantially improved

GFS T2m: EXP-CTL

GFS T2m (K) at Utica, NYCTL: Black; EXP: Red ; Obs: Blue

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GFS Test: Comparison of T1, T2m

and Tskin

for CTL and EXP

T1: Temperature at the lowest model level (Blue); T2m

: Red; Tskin

: Black

GFS: CTL GFS: EXP

Rapidly cooling: Decoupled Improvement

CTL: Large difference between T1 and T2m

with a cessation of turbulent transport between

the surface and the atmosphere, i.e., discontinuously as a function of external parameters or loss of predictability.EXP: Substantially improved not only T

2m, but also T

skin and T1.

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Summer: Surface temperature and its RMSE Northwest

Reduced warm bias in the morning and cold bias in the afternoon (1.5 °C);Reduced RMSE afternoon and nighttime up to 1.2 °C .

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Summer: Temperature fits to Obs: Bias and RMSE at fh48 NH

Reduced temperature bias and RMSE near the surface

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Reduced cold bias afternoon and nighttime (~ 1.2 °C);Reduced RMSE afternoon and nighttime up to 1.0 °C (~ 25% RMSE).

Winter: Surface temperature and its RMSE Northwest

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Precipitation Skill Scores over CONUS: f12-f36, f36-f60, f60-f84

Winter:

Improved scores for light and

medium precipitation.

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Summary

➢ Several satellite data sets developed recently (e.g., GVF, snow, burning area, albedo, radiation, soil and vegetation type) were tested in the NCEP models and the results show good improvements, compared with the current data sets;

➢ The SMOPS soil moisture data assimilation in GFS is continuously examined; Snow/burned data used in NAM.;

➢ Large cold bias of surface temperatures in GFS was identified and substantially reduced by the proposed solution;

➢ We have been continuing our efforts and working with many research teams to improve satellite data utilization over land in NCEP data assimilation system and then improve the GFS numerical weather prediction (NWP).

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