Land data assimilation in the NASA GEOS-5 system: Status and challenges
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1 Blueprints for Next-Generation Data Assimilation Systems Boulder, CO, USA 8-10 March 2016 Land data assimilation in the NASA GEOS-5 system: Status and challenges Rolf Reichle Clara Draper, Ricardo Todling, Amal El Akkraoui Global Modeling and Assimilation Office, NASA/GSFC
2 Land Observations in Global Assimilation Systems Variable Sensors Latency Period Usage Notes Precipitation In situ Days -now Reana. Gauge density. IR/MW sat. NRT Retrieval errors over land. T2m, Q2m In situ NRT -now NWP Indirect but useful. Snow depth In situ NRT -now NWP Snow cover VIS sat. NRT 80s-now NWP Clouds. Snow mass MW sat.? 80s-now Retrieval errors. Surface soil moisture MW sat. NRT (limited) 80s-2010 (C-band & up), 2010-now (L-band & up) NWP Climatology required. Skin temperature IR sat. NRT 80s-now Clouds. Difficult to assimilate. Terrestrial water storage GRACE Months 2002-now Very coarse resolution in time and space. FPAR (vegetation) VIS sat. NRT 80s-now Clouds. Helps w/ carbon fluxes, limited impact on weather.
3 3 Outline 1. SMAP Level 4 Soil Moisture Product 2. Lessons Learned 3. Looking Ahead
4 Motivation SMAP L-band (1.4 GHz) radiometer Soil Moisture Active Passive Mission Science Objectives Link terrestrial water and carbon cycles. Enhance weather and climate forecast skill. Improve flood prediction and drought monitoring. Launched 31 Jan 2015
5 Motivation SMAP L-band (1.4 GHz) radiometer Key Objectives of the L4 Surface and Root Zone Soil Moisture (L4_SM) product: Root zone soil moisture (0-100 cm) Spatially & temporally complete Launched 31 Jan 2015 Sensitive only to surface soil moisture (0-5 cm)
6 SMAP L4_SM Algorithm Precipitation observations GEOS-5 surface meteorology GEOS-5 LDAS Catchment model 3d (distributed) EnKF spatial extrapolation, interpolation & disaggregation of assimilated observations Land model Data assimilation L4_SM Product: 3-hourly, 9-km, global, 2.5-day latency SMAP observations
7 Surface Soil Moisture Root Zone Soil Moisture Core Validation Sites Summary Metrics Horizontal Scale Number of Reference Pixels NRv4 (Model only) ubrmse [m 3 m -3 ] L4_SM Vb % Conf. Interval 9 km km km km The L4_SM beta-release product meets the 0.04 m 3 /m 3 accuracy requirement for surface and root zone soil moisture. On average and across several metrics (not shown), L4_SM is better than NRv4 for surface soil moisture, and L4_SM is comparable to NRv4 for root zone soil moisture. 7
8 Soil Moisture and Temperature Analysis (29 May 2015, 0z) O-F Tb H-pol QC based on observation and model information Surface soil moisture increments 36 km [K] 9 km Surface soil temperature increments 9 km [K] Root zone soil moisture increments 9 km [m3m-3] 8
![km [K] 9 km Surface soil temperature analysis Root](/docs-images/88/115966991/images/9-1.jpg "zone soil moisture analysis 9 km [K] 9 km [m 3 m -3")
9 Soil Moisture and Temperature Analysis (29 May 2015, 0z) 9 O-F Tb H-pol Surface soil moisture analysis 36 km [K] 9 km Surface soil temperature analysis Root zone soil moisture analysis 9 km [K] 9 km [m 3 m -3 ]
10 O F and O A Statistics 10 21z analysis
11 30 Dec 2015, 21z Analysis 11 A-F surface soil moisture O-F Tb_H A-F root zone soil moisture
12 2-day Precipitation History (30 Dec 2015) 12 BoM precip Gauge locations L4_SM precip Dec 28 Dec 29 O-F Tb_H Dec 30, 21z
13 T b Observations Minus Forecast Residuals (11 Apr 18 Sep 2015) 13 Incl. H- & V-pol, ascending & descending Mean O-F (avg=1.3 K) Std-dev O-F (avg=5.8 K)
14 T b Observations Minus Forecast Residuals (11 Apr 18 Sep 2015) Std-dev normalized O-F ~ Actual Uncertainty Assumed Uncertainty H- & V-pol Asc & Desc [dim.-less] Target value = 1 DA system overestimates underestimates actual uncertainty 14
15 Std-dev Increments (Analysis Minus Forecast; 11 Apr 18 Sep 2015) Surface soil moisture 0.03 [m 3 /m 3 ] Root zone soil moisture 0.01 [m 3 /m 3 ] 15
16 16 Outline 1. SMAP Level 4 Soil Moisture Product 2. Lessons Learned 3. Looking Ahead
17 Bias Land surface retrievals and models are usually biased Bias due to parameters, forcing, and/or model formulation. Mismatch of variables (e.g., observed vs. modeled depth). Radiances are (more or less) unbiased Locally calibrate radiative transfer model, leaving land model unchanged (e.g., SMAP L4_SM using SMOS obs). Seasonally varying bias remains, address with local observations rescaling (cdf-matching). Dynamic bias estimation and correction Methods developed for observation or model bias, but not yet used operationally
18 Error Parameters Ensemble-based methods require perturbations Land model physics are damped. It is difficult to achieve realistic spread using forcing and prognostics perturbations. Parameter perturbations or multi-model approaches have other problems. Land surface heterogeneity requires local calibration of error parameters.
19 System Spin-up and Calibration Land model spin-up requires tens of years. (with carbon, thousands of years ) Spatial heterogeneity requires local calibration need long time series. Changes in AGCM (incl. land model) or in assimilated observations require re-calibration of LDAS.
20 Impact Improvements in land states over model-only estimates are small where atmospheric forcing is of good quality. Elsewhere, lack of verification data complicates validation. Particularly for soil moisture, it remains difficult to relate improvements from land assimilation to better atmospheric forecasts (case studies, bias). The most important impact may not be in atmospheric forecasts! (E.g., moisture constraints on carbon fluxes.)
21 Land Observations Limited near-real time availability of land observations. Latency constraints for important observations, including precipitation gauges, gravimetric TWS retrievals, and data from science missions may require a new approach for NRT systems ( keep rewinding ).
22 Computing Resources SMAP L4_SM system has minimal CPU requirements (compared to atmospheric modeling & assimilation): One data-day takes ~30 min wall-time on 112 cores. (Includes eight 3-hr forecast/analysis cycles.) Most of that is modeling. (Analysis of 36-km Tb is fast). Analysis of 9-km (active-passive) Tb would take another 30 min per data-day. Limiting factors are I/O and MPI communications. Overall system scales poorly.
23 Software Engineering GEOS-5 uses a single executable for (off-line) land modeling and assimilation. Land assimilation requires lots of customization. Custom land model prognostic variables ( catchment deficit, surface excess, ). Irregular land surface tiles (computation units). GEOS-5 AGCM on cube-sphere grid. Hydrologists not traditionally skilled in large-scale computing.
24 24 Outline 1. SMAP Level 4 Soil Moisture Product 2. Lessons Learned 3. Looking Ahead
25 Coupled Land-Atmosphere Assimilation Integrate GEOS-5 LDAS into (3D-VAR GSI) ADAS LA-DAS. LA-DAS is weakly coupled: Cycling and two-way land-atmosphere feedback, but separate land and atmospheric analyses.
26 GEOS-5 ADAS (3D-VAR GSI)
27 GEOS-5 LA-DAS
28 LA-DAS Experiment Assimilate ASCAT and SMOS surface soil moisture retrievals. July - September CDF-matching based on 3-month assimilation period. ADAS similar to MERRA-2. (Except for precipitation corrections and interactive aerosols.) Output resolution: 0.5 x
29 Impact of Land Assimilation (1 Aug 2014) LH responds to soil moisture differences. Results appear reasonable.
30 T2m Evaluation Verify against 6-hourly ERA-Interim T2m analysis. (By design close to station observations where available.) Greatest RMSE changes coincide with greatest flux changes (effect is local). Impact on RMSE is mixed over US, more positive over tropical Africa.
31 Integrate LDAS into EnVAR ADAS ensemble std-dev surface soil moisture ensemble std-dev root zone soil moisture EnVAR ADAS 6 Sep 2015, 3z SMAP L4_SM Try to take advantage of atmospheric ensemble in land analysis, and of land ensemble in atmospheric analysis. [m3/m3]
32 Summary Lots of work left to do (see Part 2 Lessons learned). LA-DAS development continues.
33 33 Thanks for listening. Questions?
34 EXTRAS 34
35 Core Validation Sites 35 Model only ( NR, no assim) L4_SM In Situ
36 36 Snapshots of Geophysical Fields 1 Jun 2015, 0z 1 Sep 2015, 0z surface surface root zone root zone
37 Data Counts, O F and O A Statistics Data counts (assimilated L1C TB obs, incl. asc & desc, H- & V-pol) Residuals stats [K] 1 Apr 2015 Jul 7: Radar failure Oct 26: L1C_TB R11 R12 Dec 17: L1C_TB R12170 R Feb 2016 Seasonal changes in data counts and residuals stats. Shifts at times when L1C_TB inputs changed. Jan 1: L1C_TB R12240 R
38 O F and O A Statistics 38 21z analysis
39 Number of Assimilated Data (11 Apr 18 Sep 2015) 39 Gaps in SMOSbased rescaling files (b/c of RFI in SMOS) E.g., for N=322, assimilate one pair of H- pol and V-pol Tb every day on average. Number of assimilated L1C_TB obs in 161-day period (incl. H- & V-pol, ascending & descending)
![Std-dev Increments (Analysis Minus Forecast; 11 Apr 18 Sep 2015) Surface soil moisture [m 3 /m 3 ] Surface](/docs-images/88/115966991/images/40-0.jpg "(skin) temperature [K] Root zone soil moisture [m 3 /m 3 ] 0.03 Surface layer soil temperature [K] 1.50 0.01 1.")
40 Std-dev Increments (Analysis Minus Forecast; 11 Apr 18 Sep 2015) Surface soil moisture [m 3 /m 3 ] Surface (skin) temperature [K] Root zone soil moisture [m 3 /m 3 ] 0.03 Surface layer soil temperature [K]
![Integrate LDAS into EnVAR ADAS ensemble std-dev SWLAND [W/m 2 ] EnVAR ADAS 6 Sep 2015, 14:30z LDAS 5 Sep 2014,](/docs-images/88/115966991/images/41-6.jpg "15z Try to take advantage of atmospheric ensemble in land analysis, and of land ensemble in atmospheric")
41 Integrate LDAS into EnVAR ADAS ensemble std-dev SWLAND [W/m 2 ] EnVAR ADAS 6 Sep 2015, 14:30z LDAS 5 Sep 2014, 15z Try to take advantage of atmospheric ensemble in land analysis, and of land ensemble in atmospheric analysis.
42 L4 Algorithm Precipitation observations GEOS-5 surface meteorology GEOS-5 LDAS Catchment model 3d EnKF Land surface model Data Assimilation SMAP observations FPAR (Fraction of absorbed Photo-synthetically Active Radiation) L4_SM Product: Surface and root-zone soil moisture and temperature, land surface fluxes, etc. [9 km, 3-hourly, global] Applications Users Land carbon model L4_C Product: Net Ecosystem Exchange, surface soil organic carbon, component carbon fluxes, etc. [9 km, daily, global] U Montana Terrestrial Carbon Flux model 3
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