Testing and Evaluation of GSI Hybrid Data Assimilation for Basin-scale HWRF: Lessons We Learned
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1 4th NOAA Testbeds & Proving Ground Workshop, College Park, MD, April 2-4, 2013 Testing and Evaluation of GSI Hybrid Data Assimilation for Basin-scale HWRF: Lessons We Learned Hui Shao1, Chunhua Zhou1, Mrinal K. Biswas1, Ligia Bernardet2,4, Brian Etherton2 In collaboration with Mingjing Tong3, John Derber3, Jeff Whitaker2, and Henry Winterbottom2 1National 2NOAA 3NCEP 4Cooperative Centersfor Atmospheric Research (NCEP), Boulder CO Earth System Research Laboratory (ESRL), Boulder, CO Environmental Modeling Center (EMC), College Park, MD Institute for Research in the Environmental Sciences (CIRES), CU, Boulder CO Special acknowledgement to HFIP
2 Mechanism for DTC Data Assimilation(DA) T&E DTC real-time & retrospective GSI runs using Benchmark functionally-similar operational environment: Parallel run Benchmark Focus on testing incremental changes. config Developmental Archived config Real-time: sync testbed, uncover the data / (suggested background issues from the DTC) for retro runs Short-term retrospective: test individual changes, tackle the issues Extensive retrospective: impact study w/ SS, test research/developmental components Pathway to O Operational GSI implementation and parallel test runs. Focus on evaluating the overall performance of GSI. 2
3 GSI 3D-Var/Hybrid Ensemble-3DVar Cost Functions Fit to background Fit to observations x : Analysis increment (xa xb) ; where xb is a background Bf : (Fixed) Background error covariance (estimated offline) HBf: :Observations (forward) operator (Fixed) background-error covariance (estimated offline) RB:ens Observation error covariance (Instrumentcovariance + representativeness) : (Flow-dependent) background-error (estimated from ensemble), where yo are the observations β: Weighting factor (0.25 means total B is ¾ ensemble). Cost function (J) is minimized to find solution, x [xa=xb+x ] (Courtesy from Jeff Whitaker, GSI Tutorial, 2012) 3
4 NCEP Dual-Res Global Coupled Hybrid member 2 forecast recenter analysis ensemble member 1 forecast EnKF member update member 3 forecast high res forecast Previous Cycle GSI Hybrid Ens/Var member 1 analysis member 2 analysis member 3 analysis high res analysis Current Update Cycle (From Daryl Kleist, AMS, 2011) Gets flow dependent background error covariance in 3(4)D-Var by using an ensemble estimate. Ensemble perturbations are incorporated directly into cost function using extended control variable approach 4
5 Minimal GSI-Hybrid System for Regional Applications recenter analysis ensemble Themember minimal 1 system was set up by NCEP/EMC in 2012: member 1 forecast BE contributions: 25% (β) static (fixed) and 75% ensemble analysis The ensemble input comes from NCEP s Global Forecast System member 2 member 2 (GFS) ensemble. EnKF forecast analysis member update No feedback from deterministic analysis to ensemble analyses No extra computational cost due to ensemble generation member 3 member 3 forecast analysis! Similar regional GSI-hybrid DA system has shown positive impacts in GSI NCEP s high res NAM applications. high res Hybrid Ens/Var forecast it be beneficial analysis! Would to TC forecasts? Issues? Limitations?! What developmentalcurrent direction should be taken for this specific Previous Cycle Update Cycle scenario? Regional forecast GSI Hybrid Ens/Var Regional analysis 5
6 Objectives In coordination with other regional GSI-hybrid DA development teams, the DTC! Tests and evaluates the developmental GSI-hybrid DA model, currently the NCEP minimal regional GSI-hybrid, for Hurricane WRF (HWRF) applications.! Cross covariance of variables through hybrid/ensemble components! Sensitivity study of the weights for the static and ensemble BE statistics! Cycling schemes of the DA-forecast system.! Develops develop user related interface.! Binary capability of the HWRF components! Reading big-endian files on little-endian platforms! Leads the effort to make a code management plan for the GSI-hybrid code, including both variational and ensemble components. 6
7 Hurricane WRF components HWRF Components GFS Ensemble GSI-hybrid WRF model Pre-Processor (WPS) Vortex initialization Data assimilation (GSI) Coupler (NCEP) Ocean (POM-TC) Post-Processor (UPP) Vortex Tracker (GFDL) (Courtesy from Ligia Bernadet) 7
8 2012 HWRF Basin Scale T&E Configuration Operational HWRF atmospheric config: Horizontal grid spacing: 27, 9, 3 km Inner nests move to follow storm Domain location vary from run to run depending on storm location 42 vertical levels Model top 50 hpa Exp. HWRF atmospheric config: Horizontal grid spacing: 27 km No inner nests yet Domain is fixed 61vertical levels Model top 2 hpa 8
9 Experimental Design BE weighting experiments Cycling experiment Config. ID % Static BE % Ens. BE CTL ICs GFS analysis COLD 25% 75% Cold start with GFS forecasts CYC 25% 75% 1-day GSI cycling prior to analysis time Var Cold start with GFS forecasts Var Cold start with GFS forecasts Var Cold start with GFS forecasts Var Cold start with GFS forecasts Var Cold start with GFS forecasts Only conventional observations and TCvital were assimilated. 9
10 Testing Period Dates: Aug 1st 2012 Aug 13; Aug 22nd Aug 28th, 2012 Storms covered: Ernesto, Florence, Helene, Isaac, Joyce Type/ Cat Name Dates Max Wind Min Press Deaths (mph) (mb) U.S. H2 Ernesto 1 10 Aug TS Florence 3 6 Aug TS Helene Aug H1 Isaac 21 Aug 1 Sep TS Joyce Aug $2.35B * Info from NWS National Hurricane Center webpage Isaac Helene Damage Florence Ernesto Joyce 10
11 Minimal GSI-hybrid Versus GFS CTL COLD CYC Aggregated track errors CTL COLD CYC Aggregated intensity abs. errors # of cases:
12 Hurricane Isaac Tracks Best Track CTL COLD CYC
13 Isaac Analysis Initiated at Obs Location 1000 hpa Specific Humidity CTL COLD-CTL CYCL-CTL
14 Isaac 72hr Forecasts Initiated at hpa Specific Humidity CTL COLD-CTL CYCL-CTL
15 Isaac Analysis Initiated at CTL Tropospheric deep-layer mean (DLM) wind vector and speed (kts) COLD-CTL CYCL-CTL
16 Isaac 72hr Forecast Initiated at CTL Tropospheric deep-layer mean (DLM) wind vector and speed (kts) COLD-CTL CYCL-CTL
17 Varying Weights of Static and Ensemble BE 300 Track Errors Var00 Var10 Var25 Var50 Var * Var 25: 25% Static BE and 75% ensemble BE Error (nm) Error (nm) 250 Intensity Errors
18 Isaac: Vertical profiles of RMSEs for q and T Analyses Var00 Var75 Var75 Var75 Var00 Bkg Var00 Var10 Var25 Var50 Var75 Var00 Ensemble contributions degrade analysis of T at most levels and q at low levels. Similar results were found in biases and for Ernesto. * Var 25: 25% Static BE and 75% ensemble BE 18
19 Ensemble spread (Isaac) q u T v 19
20 Summary and Conclusions! DTC built and configured a testing environment similar to NCEP/EMC (Same system, script, input data, different linux machines).! The current GSI-hybrid system for basin-scale HWRF shows minimal or no improvement on TC intensity and track compared with GFS analysis initiated forecasts.! Cycling the minimal GSI-hybrid system shows negative impacts on TC tracks and intensity by average. These impacts might be related to limitation of the DA in ocean areas? Further study is needed.! No significant impacts on TC track and intensity from changing the relative weights of the static and ensemble BE statistics.! For the case study, increased weighting of ensemble BEs gives more degradation to the biases and RMSEs of T at most levels and q at lower levels. However, ensemble contributions help the q bias at higher levels. The GFS ensemble used here should be further examined for representing regional errors? 20
21 Future Work! Regional ensemble versus global ensemble! Ongoing effort over NOAA/PSD on developing regional EnKF for regional ensemble update! Radiance/Cloudy radiance DA! NCEP EMC added new bias correction scheme for regional radiance DA! Ongoing effort on cloudy radiance over NCEP/EMC and other development teams! Moving nests/high resolution DA! Observation impact study for TC forecasts 21
22 Pseudo-single Obs Test q=1g/kg at 700mb at 28.9N, 270.5E (Isaac center) Analysis increments of specific humidity Analysis increments of temperature 22 3DVAR Hybrid (0.25) Ensemble 22
23 Real Obs Test background 3DVAR-Background AMSU-A radiance channels at 272E, 25.12N ENS-Background Hybrid-Background 23
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