NASA ROSES 2007:Application of Satellite Data to Enhance FAA Tactical Forecasts

of Convective Initiation and Growth

John R. Mecikalski, Wayne M. Mackenzie University of Alabama in Huntsville

Haig Iskendarian, Marilyn Wolfson, Charles Ivaldi Massachusetts Institute of Technology, Lincoln Laboratory

Gary JedlovecNASA Marshall Space Flight Center, Huntsville, Alabama

Outline

• Overview of Applied Science Problem: Satellite CI Nowcasting and SATCAST

• Proposed PlansProject goalsConvective regime: DefinitionNASA “A-Train” dataDatabase formulationCase analyses

• Progress to Date & Timeline

Courtesy, NCAR RAP

Lightning:Type (CG, IC, CC)AmountPolarityAltitude in cloudswith respect to anvil

Ambient Environment:Thermodynamic profile(i.e. tropical vs. midlatitude)CAPE (also, its shape) & CIN

Cumulus:Cloud-top TCloud growth rateCloud glaciationFreezing level: warm rain process ice microphysicsInteractions with ambient clouds (pre-existingcirrus anvils)

Models/Sounders

Satellite: VIS & IR

LMA & NLDN

CI, LIRainfall

What are the

factors?

ROSES 2007

SATCAST

CI Interest Field Purpose and Resolution MB06 Critical Value

6.5 Š 10.7 µm difference (IF1)

4 km cloud-top height relativeto upper-troposphericWV

weighting function(Schmetz et al. 1997)

Š35 C to Š10 C

13.3 Š 10.7 µm difference (IF2)

8 km cloud-top heightassessment

(Mecikalski and Bedka 2006;Mecikalski et al. 2008)

Š25 C to Š5 C

10.7 µm TB (IF3) 4 km cloud-top glaciation(Roberts and Rutledge 2003) -20 C < TB < 0 C

10.7 µm TB Drop Below 0 C (IF4) 4 km cloud-top glaciation(Roberts and Rutledge 2003) Within prior 30 mins

10.7 µm TB Time Trend (IF5, IF6)4 km cloud-top growth

rate/updraft strength(Roberts and Rutledge 2003)

< Š4 C/15 minsĘTB/30 mins < ĘTB/15 mins

6.5 Š 10.7 µm Time Trend (IF7) 4 km multi-spectral cloud growth(Mecikalski and Bedka 2006) > 3 C/15 mins

13.3 Š 10.7 µm Time Trend (IF8)8 km multi-spectral cloud growth

(Mecikalski and Bedka 2006;Mecikalski et al. 2008)

> 3 C/15 mins

SATCAST Algorithm: GOES IR Interest Fields

LI Interest Field Threshold Value

10.7 m TB 260 K

10.7 m 15 minute trend –10 K

10.7 m 30 minute trend –15 K

6.5 – 10.7 m channel difference –17 K

6.5 – 10.7 m 15 minute trend 5 K

13.3 – 10.7 m channel difference –7 K

13.3 – 10.7 m 15 minute trend 5 K

3.9 m fraction reflectance 0.05

3.9 – 10.7 m trend t – (t-1) –5 K and t – (t+1) –5 K

Chris Siewert/UAH - 2007/2008

SATCAST Algorithm: LI Interest Fields

MSG satellite related IR interest field that were preliminarily evaluated for use within theMB06 algorithm. A total of 20 possible indicators are being considered, plus reflectanceinformation from the 0.6, 0.8 and 1.6 m channels on MSG.

CI Interest Field Critical Value Physical Interpretation& Comments

[1] 10.8 µm TB [IF1] < 0 C Cloud-top coldness

[2] 10.8 µm TB Time Trend [IF2, IF3] < Š4 C/15 minsĘTB/30 min < ĘTB/15 min

Cloud growth rates

[1] 10.8 µm TB drop to <0 C [IF4] Within prior 30 mins Cloud-top glaciation

[1] 6.2 Š 10.8 µm difference [IF5] Š30 C to Š10 C Cloud growth into dry air aloft

[1] 6.2 Š 10.8 µm Time Trend [IF6] > 2-3 C/15 mins Cloud growth rates into dry air aloft

[2] 13.4 Š 10.8 µm difference [IF7]12.0 Š 10.8 µm difference

Š25 C to Š5 C0 to Š3 C

Cloud growth information, intomid- and upper troposphere (redundant)

[2] 13.4 Š 10.8 µm Trend [IF8]12.0 Š 10.8 µm Trend

> 3 C/15 mins> 1 C/15 mins

Cloud growth rate information, intomid- and upper- troposphere (redundant)

[1] 3.9 Š 10.8 µm difference [IF9] Transition across 0 Cin 15-30 min

Cloud-top glaciation(see also Lensky andRosenfeld 2008)

[1] 3.9 Š 10.8 µm Time Trend [IF10] > |-5| C Cloud-top glaciation(see also Lensky and Rosenfeld 2008)

[1] 7.3 Š 10.8 µm difference [IF11] Š40 C to Š15 C Cloud growth into dry air aloft(may be redundant with 6.2Š10.8 µm)

[1] 7.3 Š 10.8 µm Time Trend [IF12] > 3-4 C/15 mins Cloud growth rates into dry air aloft(may be redundant with 6.2Š10.8 µm)

[1] 1.6 Š 0.8 µm difference [IF13] Look-up table for microphysicsand glaciation (0-1)

Cloud-top glaciation(daytime only)

[1] 1.6 Š 0.8 µm Time Trend [IF14] Positive trend towards +1 Cloud-top glaciation rates(daytime only)

[1] 8.7 Š 10.8 µm difference [IF15]8.7 Š 12.0 µm difference

Look-up table for microphysics;< 0 C (use existing product)

Cloud-top glaciation towardsprecipitation formation

[1] 8.7 Š 10.8 µm Time Trend [IF16] -2-5 C/15 min Cloud-top glaciation rates towardsprecipitation formation

[1] 6.2 Š 7.3 µm difference [IF17] Positive difference Assessing if cumulus brokecapping inversion

[20] CI Indicators

COPSdata

analysisongoing

forGOES-R

SATCAST Algorithm: MSG CI Interest Fields

SATCAST Algorithm: Onward to MTG…

Applied Science Problem

• Currently, the GOES CI algorithm suffers from low predictive skill scores due to its lack of tuning to the different convective environments present across the continental U.S. at any given time.

• NASA assets are optimizing the GOES-based CI method via the in-line convective-regime “training” information they provide via a multi-parameter database—statistical look-up table approach.

• Satellites are the primary source of CI (pre-radar echo) information, representing a powerful capability to improve fine-scale convective-scale forecasts, and therefore the utility of the DSS.

DSS: Corridor Integrated Weather System (CIWS) as the cornerstoneof the FAA Consolidated Storm Prediction for Aviation (CoSPA)

Proposed Plan & Goals

1. Data collection and analysis towards SATCAST algorithm optimization

2. Ground-based validation over UAHuntsville region (i.e. CI regime definitions, dual polarimetric radar & lightning assessments of satellite-observed clouds)

3. DSS integration

4. Benchmarking improvements to the DSS

Convective Regime DefinitionsFor this effort, a “convective regime” is defined as an environment in which deep convection is supported (thermodynamically, dynamically). Therefore, the environment possesses adequate CAPE, with the vertical wind shear and momentum properties acting with the thermo-dynamics, to organize convection in nearly predictable behaviors.

It is recognized that convection initiates differently across regimes, and subsequently, observations of clouds from satellite in various regimes will be different.

Infrared and visible reflectance observations from satellite of growing clouds therefore should be optimized to environmental parameters, IF a satellite-based CI algorithm is to perform optimally.

NASA “A-Train” DataToward constraining geostationary infrared and visible fields, data from other satellite sensors and numerical weather prediction models are used. In particular, MODIS/Aqua, CloudSat and CALIPSO data are being collected in concert with GOES object-centered observations of growing cumulus clouds, that develop into thunderstorms. Meteosat Second Generation data are also being considered.

GOES (MSG) visible & infrared Interest Fields Coupled to:

(a) CloudSat and CALIPSO (for the LIDAR field) CTH estimates (via the “2B-GEOPROF” 2B-GEOPROF-LIDAR” products) and estimates of CTG (i.e. using product “2B-CWC-RO”, “2B-CWC-RVOD” as means of assessing ice at cloud-top) for cumulus (with cloud classification coming from “2B-CLDCLASS”);

(c) CTT estimates, and to some extent CTH, obtained via MODIS and the “MODIS-AN” (Aqua) product as part of the CloudSat suite;

(d) NWP thermodynamic profiles at 12-40 km resolution near active convection;

(e) NWP-derived stability indices (e.g., convective available potential energy—CAPE; lifted index—LI, etc.);

(f) Lightning (cloud-to-ground via NLDN, or total lightning via LINET or other lightning mapping array datasets.

NASA “A-Train” Data

Database Development

One main goal of this effort is to construct a database that can be mined to retrieve robust statistical relationships between the sets of CI (and LI) interest fields from GOES (and MSG) and other NWP and satellite-based variables.

This will allow for the optimization (i.e. appropriate weightings per interest field, use of selected fields within a given convective environment) of SATCAST across many regions of North America, and certainly elsewhere CI occurs.

Improved skill scores (POD, FAR, Threat, Heidke Skills) for 0-1 h CI (and LI) nowcasting, both day and night, is the expected outcome.

GIF1

GIF2

GIF3

GIF4

GIF5

GIF6

GIF7

GIF8

NWPCAPE

NWPCIN

FrzLev

CloudSat

CTTCloudSat

ICEM8.7

CALCTT

. . .

4 -2 -5 Y -5 -8 0 1 1285 -67 2813 -23 Y 237 -21 . . .

For a given CI pixel identified by GOES (or MSG)…

Case Study AnalysesFor selected regions where geostationary (GOES), A-Train, and NWP data exist, perform detailed analyses for CI/LI events in detail, so to assess the physical relationships between the satellite and NWP information and precipitation development.

Incorporate ground-based radar and lightning data.

Likely Candidate Regions:

• Cape Verde Islands/NAMMA• Puerto Rico• Guam• Belize• North Alabama, annually• Alaska region• VORTEX II• IHOP 2002

Progress to Date & TimelineStarting with 1 March 2008: Collect/process data up until current. Set up to run in real-time; work backwards through late 2006 (when CloudSat was launched).

Now identifying cumulus clouds via Berendes et al. (2008) cumulus mask.

Developing database over all of North America, and soon for MSG over NAMMA region, and likely MTSAT over South Korea (Two WSR-88D’s are present in South Korea, but depends on acquiring MTSAT data).

Now processing convective cloud mask: Assess regions of growing cumulus within 10 km of A-Train overpass (and +/- 15 minutes).  If a cumulus cloud is present, SATCAST will be processed over that region.

Major data archiving started in Fall 2008, of CloudSat, GOES, MODIS and soon CALIPSO.

First case study analysis to be completed: December 2009.

Database development and construction: Summer 2009.