Satellite Based Nowcasting of Convection Initiation and Data Assimilation

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transitioning unique NASA data and research technologies to the NWS

Satellite Based Nowcasting of ConvectionInitiation and Data Assimilation

John R. Mecikalski1, Kristopher M. Bedka2

Simon J. Paech1, Todd A. Berendes1, Wayne M. Mackenzie1

1Atmospheric Science DepartmentUniversity of Alabama in Huntsville

2Cooperative Institute for Meteorological Satellite StudiesUniversity of Wisconsin-Madison

Supported by:NASA New Investigator Program (2002)NASA ASAP, SERVIR & SPoRT Initiatives

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Outline

• Convective Initiation research, validation & transition activities

• NWS Products

• Soil Moisture Initialization Research (data assimilation)

• New Research, with NASA Data (lightning, MAMVs)

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Courtesy, NCAR RAP

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

Ambient Environment:Freezing level(i.e. tropical vs. midlatitude)CAPE (also, its shape)

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 & NLDNCI

What are the

factors?

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Where We Are Now• Applying CI algorithm over U.S., Central America & Caribbean• SATCAST/Flat ADAS to NWS HUN & MKX• SATCAST to NOAA/NESDIS & SPC• Validation & Confidence analysis• Satellite CI climatologies/CI Index: 1-6 h• Work with new instruments• Hydrological applications

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CI Nowcast Validation• Like any satellite-based weather decision-support product, false alarms do occur with the SATCAST nowcasting product

– VIS/IR Satellite observations only provide a “view from the top” and cannot retrieve in-cloud dynamics or thermodynamics, which can greatly influence cumulus evolution

– Cloud tracking errors using MESO AMVs

• An objective quantitative validation of the SATCAST nowcast product is challenging for several reasons

1) Parallax viewing effect causes difficulty in matching satellite observations with radar observed precipitation fields

2) Objective synchronization of current satellite cloud growth trends with future radar observations

3) Satellite navigation issuesParallax Shift

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SATELLITE AMV WOULD INCORRECTLY FORECAST

FUTURE RADAR ECHO LOCATION

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Correlations IF1 IF2 IF3 IF5 IF6 IF7 IF8IF1 1.000 — — — — — —IF2 0.8581 1.000 — — — — —IF3 -0.9388 -0.8690 1.000 — — — —IF5 -0.2411 -0.3304 0.2818 1.000 — — —IF6 -0.4403 -0.4989 0.5143 0.5976 1.000 — —IF7 0.1405 0.2246 -0.1358 -0.9323 -0.5029 1.000 —IF8 0.0914 0.3405 -0.0892 -0.7743 -0.3654 0.7826 1.000

ExVarPC#1 23.745 12.318 -33.025 -7.552 -16.327 4.048 2.986PC#2 10.425 1.758 -11.616 22.017 22.970 -17.277 -13.936PC#3 7.712 5.807 -1.729 -17.353 34.192 15.330 17.877PC#4 -1.057 -28.687 -11.201 -17.045 5.090 10.407 -26.514PC#5 -39.710 6.427 -28.009 -4.775 4.812 -9.567 6.700PC#6 -6.859 34.038 7.016 -5.932 2.323 14.112 -29.721PC#7 -7.123 -5.950 -9.345 33.245 -0.824 39.048 4.465

PC Eigenvalue % Variance1 396.87 67.642 122.21 20.833 42.57 7.264 11.59 1.985 8.93 1.526 2.66 0.4547 1.89 0.322

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InterestField

IF1 IF2 IF3 IF4 IF5 IF6 IF7 IF8 Mean

IF1 0.751 0.700 0.449 0.217 0.470 0.555 0.511 0.414 50.83%IF2 0.883 0.424 0.212 0.477 0.635 0.539 0.412 53.51%IF3 0.449 0.216 0.295 0.339 0.319 0.255 34.33%IF4 0.231 0.157 0.175 0.167 0.134 18.85%IF5 0.542 0.375 0.529 0.411 40.69%IF6 0.730 0.422 0.327 44.46%IF7 0.604 0.430 43.99%IF8 0.481 35.80%

Max: 53.51%

Conditional POD skill scores

CI Nowcast Validation

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Conditional FAR skill scores

InterestField IF1 IF2 IF3 IF4 IF5 IF6 IF7 IF8 MeanIF1 0.670 0.607 0.364 0.164 0.430 0.505 0.457 0.355 44.42%IF2 0.828 0.339 0.164 0.447 0.611 0.498 0.352 48.08%IF3 0.364 0.162 0.239 0.277 0.255 0.193 27.42%IF4 0.179 0.114 0.129 0.121 0.095 14.10%IF5 0.554 0.394 0.523 0.398 38.74%IF6 0.753 0.415 0.319 42.56%IF7 0.591 0.402 40.80%IF8 0.463 32.21%

Min: 14.10%

CI Nowcast Validation

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SATCAST Algorithm“Interest Field” Importance

CI Interest Field Critical Value

10.7 µm TB (1 score) < 0° C

10.7 µm TB Time Trend (2 scores)< -4° C/15 mins

∆TB/30 mins < ∆TB/15 mins

Timing of 10.7 µm TB drop below 0° C (1 score) Within prior 30 mins

6.5 - 10.7 µm difference (1 score) -35° C to -10° C


6.5 - 10.7 µm Time Trend (1 score) > 3° C/15 mins


• Deep convection, dry upper troposphere.• Best for high CAPE environments, and strong updrafts.• Winter-time, Midlatitudes

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SATCAST Algorithm“Interest Field” Importance


10.7 µm TB (1 score) < 0° C








• Moist upper troposphere, “warm-top” convection. Shallow convection.• Low CAPE environments (tropical, cold-upper atmosphere). f(Physics)• Optimal in Tropics during summer.

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SATCAST Algorithm“Interest Field” Importance: POD/FAR


10.7 µm TB (1 score) < 0° C








• Use of 8.5-11 & 3.7-11 m from MODIS have been considered• Instantaneous 13.3–10.7 um: Highest POD (88%)• Cloud-top freezing transition: Lowest FAR (as low as 15%)• Important for CI & Lightning Initiation

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NWS Transition ActivitiesSATCAST in AWIPS

Web Survey 2007

NESDIS Operations

U. Wisconsin - CIMSS collaboration

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Outline

• Convective Initiation research, validation & transition activities

• NWS Products

• Soil Moisture Initialization Research (data assimilation)

• New Research, with NASA Data (lightning, MAMVs)

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NWS Transition ActivitiesFlat ADAS for Surface Analyses

Flat ADAS

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NWS Transition ActivitiesMesoscale AMVs: 2007-2008

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NWS Transition ActivitiesCI “Trends of Trends”: 2007-2008

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Satellite-Lightning Relationships• Current Work: Develop relationships between IR TB/TB trends and lightning source counts/flash densities toward nowcasting (0-2 hr) future lightning occurrence* Supported by the NASA New Investigator Program Award #:NAG5-12536

2047 UTC

2147 UTC

Northern Alabama LMA Lightning Source Counts

2040-2050 UTC

2140-2150 UTC

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GOES CI Interest Fields: 21 July 2005 (afternoon)Details:-topography-main updrafts

…for LI1 km Resolution

CI Climatology Research

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IR Window TB

8.5-

11 μ

m D

iffe

renc

e

Deep Cu/Ice Cloud

Cirrus Edge/Mid-Height Cu

Clear Sky/Small Cu

Semi-Transparent CirrusJava-based Hydra Visualization Tool

Multispectral cloud properties are used to classify cumulus and identify clouds in a pre-CI state

8.5-11 m Difference vs IR Window TB

GOES-R Risk Reduction

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GOES-R Risk Reduction

Preliminary MSG CI Nowcasting Criteria


10.8 µm TB < 0 K

10.8 µm TB Time Trend< -4 K/15 mins

TB/30 mins > TB/15 mins

Timing of 10.8 µm TB drop below 0° C Within prior 30 mins

8.5-10.8 µm TB Difference < 0 K

12.0-10.8 µm TB Difference* -3 to 0 K

CAPE > 500 J/kg Microphysical information from 1.6 reflectance is used to improve Microphysical information from 1.6 reflectance is used to improve the convective cloud mask and negative 8.7-10.8 the convective cloud mask and negative 8.7-10.8 m differencing m differencing values are used to identify cumulus with liquid water topsvalues are used to identify cumulus with liquid water tops

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MODIS/GOES Convective Cloud Mask Validation

Visible Channel GOES Convective Cloud Mask

MODIS Convective Cloud Mask

a) c)b)

d) f)e)

Visible ChannelGOES Convective Cloud Mask


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a) c)b)

d) f)e)

In preparation for GOES-R

Visible Channel MSG Convective Cloud Mask


Visible ChannelMSG Convective Cloud Mask


MODIS/GOES Convective Cloud Mask Validation

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1 Aug 2004 Soil Moisture Differences

0-10 cm 10-40 cm

40-100 cm 100-200 cm

ALEXI - EDAS

• The largest differences between ALEXI and EDAS soil moisture occur over the eastern half of the study domain

• The 40-100 cm soil layer shows that ALEXI soil moisture is wetter across a majority of the domain

• The drier conditions in the 100-200 cm soil layer are once again in a region where vegetation is not able to extract water from the soil. The wetter conditions in SE OK are located within vegetation types which can extract water from this layer.

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ALEXI and EDAS Comparisons

ALEXI

RMSE = 0.059 or 19.7% fAW

EDAS

RMSE = 0.095 or 31.7% fAW

The retrieval of ALEXI soil moisture is compared to soil moisture observations from the Eta Data Assimilation System (EDAS) for each of the composite periods.

The EDAS soil moisture show substantial dry biases, with the largest bias occurring during observed wet soil moisture conditions (high fPET).

With respect to all observations during this period, the ALEXI soil moisture retrieval produces soil moisture estimates that exhibit a much lower RMSE than EDAS.

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Future Work• Continue AWG GOES-R Risk Reduction• Further Satellite based lightning initiation research using GOES, MODIS

& MSG• Thru NASA SERVIR, provide more hydrologic information from SATCAST• Using MODIS NDVI product and topography maps, improve nowcasting

0-3 hours using vegetation and topography to determine areas where CI may occur. [John Walker, UAH]

• CloudSat, MODIS & QuikSCAT for convective momentum fluxes and mesoscale AMV assimilation [Chris Jewett, UAH]

• Peer-reviewed papers• Additional soil moisture assimilation work: AMSR+MODIS via ALEXI

[Chris Hain, UAH]; with the USDA• Convective Climatologies [2 UAH Graduate Students]• Collaboration with SPC [late 2007] & NOAA NESDIS• Continued SATCAST validation + transfer of MAMVs to NWS Huntsville

Satellite Based Nowcasting of Convection Initiation and Data Assimilation

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