Preliminary Results of a U.S. Deep South Warm Preliminary Results of a U.S. Deep South Warm Season Deep Convective Initiation Modeling Season Deep Convective Initiation Modeling Experiment using NASA SPoRT Initialization Experiment using NASA SPoRT Initialization Datasets for Operational National Weather Datasets for Operational National Weather

Service Local Model RunsService Local Model Runs

Jeffrey M. Medlin1, Lance Wood2, Brad Zavodsky3 Jon Case4 and Andrew Molthan3

1NOAA National Weather Service; Mobile, AL2NOAA National Weather Service (NWS); Houston, TX

3NASA SPoRT Center/Marshall Space Flight Center; Huntsville, Alabama4 NASA Short-term Prediction Research and Transition (SPoRT) Center/ENSCO, Inc.; Huntsville,

Alabama

2012 NASA SPoRT Virtual Partner’s Workshop2012 NASA SPoRT Virtual Partner’s Workshop13 Sep 2012

Funct

(1) Function of Seasonal Progression

(2) Function of Boundary Layer Convergence and Wind Flow

over Local Terrain

The Convective Initiation Forecast ProblemThe Convective Initiation Forecast Problem

Summer

SpringStronger Land-SST GradientStronger Land-SST Gradient

Weaker Land-SST GradientWeaker Land-SST Gradient

Medlin and Croft, 1998Medlin and Croft, 1998Medlin and Croft, 1998Medlin and Croft, 1998

Medlin and Croft, 1998Medlin and Croft, 1998

(3) Surface (3) Surface Processes?Processes?

ObjectivesObjectives

Objectively quantify impacts of NASA datasets (LIS, MODIS SSTs, GVF) on the summertime deep convective initiation mesoscale modeling problem. Will perform objective verification – more later.

◦ Are surface processes (e.g., LH and SH fluxes, soil moisture, soil temperature) and ambient ingredients better represented in the initialization that, in turn, will improve timing and location of the first initiates?

Highlight how a NWS Operational Meteorologist-Researcher collaboration such as this can be invaluable towards addressing forecast problems.

Hopefully this collaborative approach can set a precedent for how local and/or regional mesoscale modeling may be approached in the future!

MethodologyMethodology

Using identical model settings on two separate WRFEMS-ARW Core domains, the NWS Mobile and Houston offices are concurrently evaluating the impacts of the following NASA SPoRT data sets on the summertime weak vertical wind shear deep convective initiation problem:

SPoRT SSTs – 2 km sea-surface temperature analysis, updated twice daily. LIS - 3 km land information system, updated four times daily. GVF – 1 km green vegetation fraction, updated daily.

*** *** In other similar studies, each data set has been shown to improved convective initiation forecasts.

Model SettingsModel Settings

Domains = 9 km \ 3 km Levels = 40 Time Step = 54 s Run Time = 6 UTC daily out to 24 h Initial Conditions = GFS Personal Tile (0.205°) Boundary Conditions = GFS Personal Tile (0.205°) Convective Parameterization = Kain-Fritsch outer Microphysics = WRF Single-Moment 6 Class Boundary Layer Scheme = Mellor-Yamada-Janjic Long-/Shortwave Radiation Schemes = RRTM, Dudhia

Experience with LimitationsExperience with Limitations

Regardless of any potential improvements discovered, the following will remain a challenge in regard to modeling the initiation of summertime deep convection with 3-4 km horizontal resolution:

Individual updrafts most often initiate too late and become too large.

Cannot just look at radar reflectivity! – must analyze ingredients, processes and character of local forcing.

Effectively evaluating model performance requires a combination of quantitative metrics and case studies.

SPoRT has tailored existing MET (Model Evaluation Tools) scripts to meet WFO needs for performing objective-based model verification.

Via examination of ‘bulk’ statistical differences [i.e., “SPoRT-Control”], and those that appear when stratified

according to various pre-existing boundary layer conditions, it is our hope to improve our physical understanding of the convective initiation forecast problem.

Objective Verification - SPoRT MET ScriptsObjective Verification - SPoRT MET Scripts

“The types of things we’re examining . . .”

Case 1Case 1 - Convective Initiation Case for Mobile-Pensacola and Mobile-Montgomery Inland Corridor, 3 July 2012

3 July 2012 – 3 July 2012 – 1858 UTC – 0.5 deg Base RefNE PBL Wind flow

- Area 2 – 18-20 UTC

- Area 1 – 17-18 UTC

18Z_CTL 1 km Radar Ref vs. 0.5 deg Lev II 18Z_OP18Z_OP 1 km Radar Ref vs. 0.5 deg Lev II

?

Very first initiatesVery first initiates

More greenedd

Surface Vegetation– 17 UTC (F11 h) Surface Vegetation– 17 UTC (F11 h) [SPoRT-CTL]

Less Greeness More Greeness

3 Jul 2012

“More Greenness” vs. Climo available for evapotranspiration

SE of dashed line.

Reflects latest drought trend well!

Mini-DroughtMini-Drought12 June – 2 July12 June – 2 July

Southern Plains RidgeSouthern Plains Ridge

Case, J. L., F. J. LaFontaine, S. V. Kumar, and G. J. Jedlovec, 2011: A Real-Time MODIS Vegetation Composite for Land Surface Models A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecastingand Short-Term Forecasting. Preprints, 15th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans and Land Surface, Seattle, WA, Amer. Meteor. Soc., 11.2

Excellent GVF Paper!

Volumetric Soil Moisture – 06 UTC (F00 h)Volumetric Soil Moisture – 06 UTC (F00 h) [SPoRT-CTL]

“Mean volume of water per soil volume over a 5 cm depth”

Drier More Moist

3 Jul 2012

Initialization

Drier than CTL inland – more moist N of sea-breeze along coast

18Z_CTL 950hPa MFC vs. 0.5 deg Lev II 18Z_OP18Z_OP 950 hPa MFC vs. 0.5 deg Lev II

Stronger Boundary Layer MFC vs. CTL

LH Flux– 17 UTC LH Flux– 17 UTC (F11 h)[SPoRT-CTL]

SH Flux– 17 UTC SH Flux– 17 UTC (F11 h)[SPoRT-CTL]

3 Jul 2012 3 Jul 2012W/m2 W/m2

TwρcH p qwρLLE v

• Determines how much heat is transferred above the surface -- important factor when predicting above-surface temperature and boundary layer depth from mixing.

Results fromResults from:

• evaporation (↑flux; moist surface)• transpiraton (↑flux; leaves)• evaporation + transpiration = evapotranspiration• condensation (↓flux; dew deposition)

Greater sensible heat flux ahead of sea-breezeAND where first initiates observed inland

Greater latent heat flux ahead of sea-breeze and where first initiates observed inlandBUT near zero to slightly lesser inland overall.

Skin Temperature (C)– 17 UTC Skin Temperature (C)– 17 UTC (F11 h)[SPoRT-CTL]

SH Flux– 17 UTC SH Flux– 17 UTC (F11 h)[SPoRT-CTL]

3 Jul 2012W/mW/m22Deg(C)Deg(C)

10-13C Since skin temp greatly affects SH Flux (wT’), difference fieldsappear very similar

Difference most noticeable over inland areas

Similar Similar

LH Flux– 17 UTC LH Flux– 17 UTC (F11 h)[SPoRT-CTL]

950 hPa Mixr– 17 UTC 950 hPa Mixr– 17 UTC (F11 h)[SPoRT-CTL]

3 Jul 2012 3 Jul 2012W/mW/m22g/kgg/kg

Lower qin generalinland

Greater latent heat flux ahead of sea-breezeBUT near zero to lesser inland where first initiates observed

Higher qin general

SBCAPE – 17 UTC SBCAPE – 17 UTC (F11 h)[SPoRT-CTL]

3 Jul 2012 3 Jul 2012J/kgJ/kg

SBCINH – 17 UTC SBCINH – 17 UTC (F11 h)[SPoRT-CTL]

J/kgJ/kg

*Less negative energy thatmechanically-forced parcel has to overcome all areas

Higher SBCAPE

Lower SBCAPE

Non-Linear Variations due to Different Non-Linear Variations due to Different Computational PlatformsComputational Platforms

Testing by Jonathan Case revealed this was a significant issue for our study, since platforms are different between SPoRT and the WFOs.

SPoRT has performed re-runs of our operational WRF for good candidate warm season CI days.

Examples of these variations from both WFO Mobile and WFO Houston follow.

July 26July 26thth 2012 Mobile 2012 Mobile

August 8August 8thth 2012 Houston 2012 Houston

Case 2 - HGX - June 28Case 2 - HGX - June 28thth 2012 Convective 2012 Convective Initiation Case Initiation Case

Case study days were selected where no significant synoptic scale forcing was present.

We wanted to focus on CI along the sea/bay breeze boundaries and with differential heating. We want to see how the model is doing with 1st generation convection.

I would subjectively have a small preference for the SPoRT

reflectivity forecast when compared to the Control for the small subset of cases that I have examined.

This particular case depicts a recurring warm season WRF issue across SW areas of the HGX CWA, where in general convection is over forecast by the model. This bias appears slightly greater in the Control run when compared to the SPoRT run.

““Typical Summer Conditions”Typical Summer Conditions”

Mid/upper ridge centered to the Mid/upper ridge centered to the north. (below)north. (below)

Low-level southeast flow off of Low-level southeast flow off of the Gulf. the Gulf.

.5 degree radial velocity 6/28 .5 degree radial velocity 6/28 (15Z) (top)(15Z) (top)

6/28 (12Z) 500mb 6/28 (12Z) 500mb geopotential height (m) (right) geopotential height (m) (right)

SPoRT 6/28/18ZSPoRT 6/28/18Z

Level II Radar Reflectivity vs. SPoRT WRF Contoured ReflectivityLevel II Radar Reflectivity vs. SPoRT WRF Contoured Reflectivity

Control 6/28/18ZControl 6/28/18Z

SPoRT 6/28/21ZSPoRT 6/28/21Z

Level II Radar Reflectivity vs. SPoRT WRF Contoured ReflectivityLevel II Radar Reflectivity vs. SPoRT WRF Contoured Reflectivity

Control 6/28/21ZControl 6/28/21Z

Level II Radar Reflectivity vs. Control WRF Contoured ReflectivityLevel II Radar Reflectivity vs. Control WRF Contoured Reflectivity

11stst and 2 and 2ndnd generation convection, with sea/bay breeze in yellow generation convection, with sea/bay breeze in yellow

outflowoutflow

SPoRT 22Z

Sea/bay breezeSea/bay breeze

Surface Vegetation SPoRT 6/28 Surface Vegetation Control 6/28

Large differences in vegetation initialization SPoRT vs. Control runsLarge differences in vegetation initialization SPoRT vs. Control runs

SFC Temp SPoRT 6/28/18Z SFC Temp Control 6/28/18Z

warmer

Warmer far inland afternoon/evening temperatures from SPoRT run vs. Control run

ReferencesReferencesCase, J. L., F. J. LaFontaine, S. V. Kumar, and G. J. Jedlovec, 2011: A real-time MODIS vegetation composite for land surface models and short-term forecasting. 

Preprints, 15th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans and Land Surface, Seattle, WA, Amer. Meteor. Soc., 11.2. Available online athttp://ams.confex.com/ams/91Annual/webprogram/Manuscript/Paper180639/Case_etal_2011AMS-15IOAS-AOLS_11.2_FINAL.pdf]

Case. J. L., F. J. LaFontaine, S. V. Kumar, and C. D. Peters-Lidard, 2012: Using the NASA-Unified WRF to assess the impacts of real-time vegetation on simulations of severe weather.  Preprints, 13th Annual WRF Users’ Workshop, P69. [Available online athttps://www.regonline.com/AttendeeDocuments/1077122/43418383/43418383_1045166.pdf]

Case. J. L., F. J. LaFontaine, J. R. Bell, G. J. Jedlovec, S. V. Kumar, and C. D. Peters-Lidard, 2012: A real-time MODIS vegetation product for land surface and numerical weather prediction models.  EEE Trans. Geosci. Remote Sens., In Review.

Haines, S. L., G. J. Jedlovec, and S. M. Lazarus, 2007: A MODIS sea surface temperature composite for regional applications.IEEE Trans. Geosci. Remote Sens., 45, 2919–2927.

LaCasse, K. M., M. E. Splitt, S. M. Lazarus, and W. M. Lapenta, 2008: The impact of high-resolution sea surface temperatures on the simulated nocturnal Florida marine boundary ayer. Mon. Wea. Rev., 136, 1349–1372.

Schiferl, L., K. K. Fuell, J. L. Case, and G. J. Jedlovec, 2010: Evaluation of enhanced high resolution MODIS/AMSR-E SSTs and the impact on regional weather forecasts. Preprints,  14th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Atlanta, GA, Amer. Meteor. Soc., P535. [Available online at http://ams.confex.com/ams/pdfpapers/163774.pdf.]

Case, J. L., W. L. Crosson, S. V. Kumar, W. M. Lapenta, and C. D. Peters-Lidard, 2008: Impacts of high-resolution land surface initialization on regional sensible weather forecasts from the WRF model. J. Hydrometeor., 9, 1249-1266.

Case, J. L., S. V. Kumar, J. Srikishen, and G. J. Jedlovec, 2011: Improving numerical weather predictions of summertime precipitation over the southeastern United States through a high resolution initialization of the surface state. Wea. Forecasting, 26, 785-807.

Kumar, S. V., and Coauthors, 2006. Land Information System - An Interoperable Framework for High Resolution Land Surface Modeling.  Environmental Modeling & Software, 21 (10), 1402-1415, doi:10.1016/j.envsoft.2005.07.004.

Peters-Lidard, C. D., and Coauthors, 2007: High-performance Earth system modeling with NASA/GSFC’s Land Information System.  Innovations Syst. Softw. Eng., 3, 157-165.