Adaptive targeting in OSSE

Outline Adaptive observing / data processing techniques in OSSE

Addition to OSSE

Link with THORPEX

Link with T-PARC

(1)Adaptive data observing/processing techniques in OSSE

Test methods/platforms/application in OSSE framework Develop software into OSSE

Ensemble (T126 or T170) product generation in OSSE ETKF targeting strategy (certain instruments)

Evaluate data impact by certain instruments like UAS, Doppler Wind Lidar

The ETKF spotted the target area

Expected error reduction propagation

Targeting methods - ETKF

Storm

Dropsondes to be made by G-IV

Global extension

Better adaptive strategy if implemented (examples)

The optimal sampling region located in the jet core

(2)Additions to OSSE Assess threat of high impact events based on ensemble

– automatically pick high impact events at 3-day leading time

Run ET/ET KF targeting for each high impact case Dispatch observing systems/data processing resources

(before and inside DA)

Wind Lidar, UAV etc. Assimilate targeted data (carry out adaptive data

processing) Evaluation (EXAMPLES NEXT FROM WSR)

Impact of Data

500mb height250mb height

Precipitation Surface pressure

Contours are 1000mb geopotential height, shades are differences in the fields between two experiments

Forecast verification

Red contours show forecast improvement due to WSR dropsondes, blue contours show forecast degradation

500mb height

250mb height

Sea Level Pressure

Forecast Verification for Wind

RMS error reduction vs. forecast lead time

10-20% rms error reduction in Temperature

60 hr forecast is equivalent to 48hr forecast

(3)Link with THOPREX THORPEX – A World Weather Research

Program (WWRP): Accelerate improvements in skill/utility of

1-14 day weather forecasts Long-term (10-yrs) global research program in areas of: Observing system, data assimilation,

numerical modeling/ensemble, socioec. appl. Strong link with operational Numerical

Weather Prediction (NWP) centers International program under WMO

THORPEX evaluation metrics (1) Possible new probabilistic guidance products for

high impact events Hydrometeorology

Extreme hydro-meteorological events, incl. dry and wet spells (CONUS)

Quantitative extreme river flow forecasting (OCONUS) Tropical / winter storm prediction

Extreme surface wind speed Extreme precipitation (related to wet spells) Storm surges

Aviation forecasting Flight restriction Icing, visibility, fog, clear air turbulence

Health and public safety Hot and cold spells

THORPEX evaluation metrics (2) “Legacy” NCEP internal probabilistic scores

to assess long-term progress General circulation

Probabilistic 1000 & 500mb height forecasts Storm

Strike probability for track Probability of intensity (central pressure or wind-based)

(4) T-PARC interestsGlobal optimal positioning of “observing” systems in OSSEImprove forecast accuracy

Day 3-4

Radiosondes

Russia

Day 3-4

GEMS

Driftsondes Aerosonde

s

D 2-3

G-IV

D 1-2

C-130

UAS

D-1

UAS

P-3

CONUS

VR

NA

VR Day 5-6Radiosonde

s

Tibet

Extensive observational platforms during T-PARC winter phase allow us to track the potential storms and take additional observations as the perturbation propagate downstream into Arctic and US continents

T-PARC PROPOSED OBSERVING PLATFORMS

Before and after field campaign “Nature” is defined as a series of states corresponding to the real atmosphere

Generated by very high resolution model runs nudged by operational analysis (GDAS)

Advantages: Use T-PARC type OSE to calibrate OSSE system –

much easier to calibrate, community will be convinced if we can reproduce their OSE work

Retrospective work after T-PARC: T-PARC represent only one configuration of global observing system, with OSSE such defined, many other configuration can be tested

This is an alternative

Advantages (more) 1)ease of calibration (one-to-one comparison, can quantitatively

evaluate osse system based on a SINGLE (or few) case(s), instead of requiring a large sample of cases

2)Close to perfect representation of model related uncertainty (osse nature made largely by nature, not a model)

3) No need to painstakingly evaluate or amend osse nature run 4) Can use humidity (cloud, moisture) observations from real world to decide if certain observations can be made or not in osse world - potentially a big contribution to making osse real life-like

5) Same nature can be redone with higher resolution or other type of model (using operational analysis as forcing) - direct comparison of different OSSE systems possible

6) Estimate how proposed new observing systems would help analysis/forecast for real life significant events (Katrina, etc)

7) Post field campaign analysis: Add significant value by osse testing of alternative deployments (after calibration in which actual and simulated field phase observations are assimilated and their impacts are compared in both OSE and OSSE framework

Concern:

Improved analysis might not mean improved forecast for individual cases We think statistically it will improve forecasts

OSSE strategy

1. Implement ET similarly as NCEP operational Ensemble forecast system

Coding development

Initial conditions (Data analysis from conventional data + radiance data assimilation)

2. Targeting strategy similarly as WSR – Identify typical storm cases in the Nature run

use targeting strategy to find sensitive areas to target

1. Increase data resolution in sensitive areas (adaptive grid)

2. Direct observation

T-PARC interests (Ideas can be tested in OSSE)

• Rossby-wave plays a major role in the development of high impact weather events over North America and the Arctic on the 3-5 days forecast time scale

• Additional remotely sensed and in situ data can complement the standard observational network in capturing critical processes in Rossby-wave initiation and propagation

• Adaptive configuration of the observing network and data processing can significantly improve the quality of data assimilation and forecast products

• Regime dependent planning/targeting • Case dependent targeting

• New DA, modeling and ensemble methods can better capture and predict the initiation and propagation of Rossby-waves leading to high impact events

• Forecast products, including those developed as part of the TPARC research, will have significant social and/or economic value

Sequence of analysis fields Dynamically consistent – NOT COMPLETELY

Lack of consistency interferes with forecast evaluation Only analysis quality can be evaluated directly NATURE MODEL CAN BE RUN ALONG WITH OSSE FCST

Dynamics/physics different from assimilating model – MOST REALISTIC REPRESENTATION OF MODEL ERRORS?

PERFECT MODEL SCENARIO NOT POSSIBLE Differences should correspond to difference between nature & our

models No difference means perfect model assumption, THORPEX interest

“Realistic” - YES Climate stats matching reality - YES

Moisture variables realistic so obs locations can be chosen realistically YES

Same weather as in nature - YES Allows direct comparison between OSSE & OSE results for reliable

calibration using small amount of data - YES