Mesoscale Cloud Mesoscale Cloud ClimatologiesClimatologies

Ken GouldKen Gould

National Weather ServiceNational Weather ServiceTallahassee, FLTallahassee, FL

Presented SatMet 00-1Presented SatMet 00-1Wednesday, 5 April 2000Wednesday, 5 April 2000

OutlineOutline

IntroductionIntroduction Types of mesoscale cloud climatologiesTypes of mesoscale cloud climatologies Channels usedChannels used Stratification and averaging intervalStratification and averaging interval ApplicationsApplications Data integrationData integration Climatology designClimatology design Lab on sea breeze climatology over the Lab on sea breeze climatology over the

Florida PanhandleFlorida Panhandle

IntroductionIntroduction

Cloud climatologies began in the late 1960’s Cloud climatologies began in the late 1960’s with analog cloud mosaicswith analog cloud mosaics

Digital data gained use in the late 1970’s Digital data gained use in the late 1970’s through the 1980’sthrough the 1980’s

However, storage requirements and lack of However, storage requirements and lack of computing power limited the development of computing power limited the development of cloud climatologies to the major universities cloud climatologies to the major universities and research centersand research centers

Now, mesoscale cloud climatologies Now, mesoscale cloud climatologies can easily and inexpensively be done can easily and inexpensively be done

locallylocally

Advent of the GOES 8/9/10 seriesAdvent of the GOES 8/9/10 series

Increased PC and Workstation storage and CPU Increased PC and Workstation storage and CPU speedspeed

Facilitated transmission of digital satellite data Facilitated transmission of digital satellite data over the internet. A Direct Readout Ground over the internet. A Direct Readout Ground Station is no longer a requirement to receive Station is no longer a requirement to receive near real time data.near real time data.

Types of Mesoscale Cloud Types of Mesoscale Cloud Climatologies (MCC’s)Climatologies (MCC’s)

General- year round digital averaging of images at General- year round digital averaging of images at the same time and time intervalthe same time and time interval– If in an area of infrequent change (equatorial regions), If in an area of infrequent change (equatorial regions),

may be a good starting point. Otherwise, not may be a good starting point. Otherwise, not recommended.recommended.

Seasonal- digital averaging of images at the same Seasonal- digital averaging of images at the same time and time interval over a particular seasontime and time interval over a particular season– Highly recommended. Allows for sufficient change in Highly recommended. Allows for sufficient change in

synoptic patterns, while still not requiring an inordinate synoptic patterns, while still not requiring an inordinate amount of time to build a significant data base.amount of time to build a significant data base.

Monthly- digital averaging of images at the same Monthly- digital averaging of images at the same time and time interval over each monthtime and time interval over each month– Also highly recommended. Provides more detail than Also highly recommended. Provides more detail than

seasonal in areas of frequent change.seasonal in areas of frequent change.– However, may take several However, may take several yearsyears to establish a to establish a

significant data base, especially if a large number of significant data base, especially if a large number of regimes is used.regimes is used.

Phenomenon driven- used specifically to observe Phenomenon driven- used specifically to observe recurring phenomena over one’s area. May also recurring phenomena over one’s area. May also be monthly, seasonal, or have some overlap.be monthly, seasonal, or have some overlap.– Probably the best approach to an MCC. Requires the Probably the best approach to an MCC. Requires the

most insight and pre-planning, but should give the most insight and pre-planning, but should give the best results. Some examples include:best results. Some examples include:

Sea BreezeSea Breeze Fog/ sea fog/ stratusFog/ sea fog/ stratus OrographicOrographic

ChannelsChannels Visible- 1.1 km or 4 kmVisible- 1.1 km or 4 km

IR - 10.7IR - 10.7uu, 4km, 4km

IR Channel 2 - 3.9IR Channel 2 - 3.9uu, 4km, 4km

Water Vapor - 8kmWater Vapor - 8km

Derived products- such as the fog Derived products- such as the fog productproduct

Stratification and Stratification and Averaging IntervalAveraging Interval

Depending on the type of climatology and intended Depending on the type of climatology and intended application, you may choose to stratify the data into application, you may choose to stratify the data into regimes based on an appropriate synoptic flow.regimes based on an appropriate synoptic flow.

For most applications, a low level wind or low Mean For most applications, a low level wind or low Mean Layer Vector Wind (MLVW) at some chosen time of Layer Vector Wind (MLVW) at some chosen time of day should be used. For example, we use the 12 day should be used. For example, we use the 12 UTC MLVW from 1000 ft to 700 mb for the Florida UTC MLVW from 1000 ft to 700 mb for the Florida Panhandle sea breeze climatology.Panhandle sea breeze climatology.– Note: SFC winds should generally be avoided since they Note: SFC winds should generally be avoided since they

may contain mesoscale contamination.may contain mesoscale contamination.

Regime development - several approaches Regime development - several approaches possiblepossible– 8 pt. Compass --> 8 regimes8 pt. Compass --> 8 regimes– Onshore/offshore/parallel to shore flowOnshore/offshore/parallel to shore flow– Upslope/downslope/parallel to slope flowUpslope/downslope/parallel to slope flow– Bin by repetitive synoptic patterns during a particular Bin by repetitive synoptic patterns during a particular

seasonseason– Incorporate flow strengthsIncorporate flow strengths– Eliminate “disturbed” daysEliminate “disturbed” days– Remember: the more regimes you choose, the more Remember: the more regimes you choose, the more

detailed your climatology will be, but the longer it detailed your climatology will be, but the longer it will take to build a significant data basewill take to build a significant data base

Averaging IntervalAveraging Interval– Dependent on applicationDependent on application– With GOES 8/9/10, 15 minute intervals possibleWith GOES 8/9/10, 15 minute intervals possible– Generally hourly to 3 hourly recommendedGenerally hourly to 3 hourly recommended

Synoptic Flow Regimes and # of Days from Synoptic Flow Regimes and # of Days from the Summers of 1995-1997the Summers of 1995-1997

Regime Description # of Days

1 Light and variable or light SE 19

2 Light to moderate E to NE 25

3 Strong (>10 kts) E to NE 11

4 Light to moderate W to SW 37

5 Strong W to SW 22

6 Moderate (6 to 10 kts) SE to S 6

7 Strong SE to S 6

8 Light to moderate N to NW 23

9 Strong N to NW 10

ApplicationsApplications

Learn more about the phenomenon being Learn more about the phenomenon being studied and its behavior in a particular areastudied and its behavior in a particular area

Can be used to help solve a myriad of forecast Can be used to help solve a myriad of forecast problemsproblems

Shows preferred areas of convective developmentShows preferred areas of convective development

21 UTC image from regime 1, light and variable to light SE flow

Shows effects of synoptic flow changes on Shows effects of synoptic flow changes on these preferred areasthese preferred areas

20 UTC image from regime 2 (light to moderate E to NEflow) on left, 20 UTC image from regime 5 (strong W toSW flow) on right

May show marked effects from small scale May show marked effects from small scale features such as bays, lakes, and riversfeatures such as bays, lakes, and rivers

18 UTC image from regime 8 (light to moderate N to NW flow)

Shows preferred areas of fog/ low cloud developmentShows preferred areas of fog/ low cloud development

Gives us insight into the timing of cloud developmentGives us insight into the timing of cloud development

Cloud frequency and probability tables can also be Cloud frequency and probability tables can also be generated (Hall, et al., 1998)generated (Hall, et al., 1998)

Can be used as a direct aid in many NWS forecast productsCan be used as a direct aid in many NWS forecast products– Aviation forecasts (tafs and twebs)Aviation forecasts (tafs and twebs)– Zone forecastsZone forecasts– Marine forecastsMarine forecasts– NowcastsNowcasts

Data Integration- Data Integration- creates powerful forecast toolscreates powerful forecast tools

Integration of complementary mesoscale Integration of complementary mesoscale climatologiesclimatologies– PrecipitationPrecipitation– Probability of Precipitation (POP)Probability of Precipitation (POP)– Radar (Hourly Digital Precipitation (HDP) data)Radar (Hourly Digital Precipitation (HDP) data)– LightningLightning– ThermodynamicThermodynamic

Use of mesoscale modelsUse of mesoscale models– Idealized runsIdealized runs– Real time modeling Real time modeling

Precipitation and POPPrecipitation and POP

Precipitation and POP for Tallahassee by regime for 1995-1997Precipitation and POP for Tallahassee by regime for 1995-1997

POP isopleths for regime 1 using Co-op sites POP isopleths for regime 1 using Co-op sites over the Florida Panhandleover the Florida Panhandle

HDP Image for Regime 8 (all hours)HDP Image for Regime 8 (all hours)

LightningLightning

Lightning flash density at 20 UTC for Regime 1Lightning flash density at 20 UTC for Regime 1

ThermodynamicThermodynamic

Use 12 UTC soundings from TLHUse 12 UTC soundings from TLH

Modify soundings to expected 18 UTC Modify soundings to expected 18 UTC conditions using SHARP Workstationconditions using SHARP Workstation

Average levels every 50 mb for both modified Average levels every 50 mb for both modified and unmodified soundings for each regimeand unmodified soundings for each regime

Analyze the mean soundings and convective Analyze the mean soundings and convective parameters such as CAPE and PWparameters such as CAPE and PW

Sounding AnalysisSounding AnalysisRegime CAPE (J / kg) Std. dev. PW (in.) Std. dev.

1 Mod 2492 683 1.65 0.35

1 Unmod 2106 945 1.74 0.36

2 Mod 2441 1329 1.62 0.29

2 Unmod 1327 1177 1.68 0.28

3 Mod 1984 864 1.61 0.33

3 Unmod 1308 888 1.70 0.33

4 Mod 3338 870 1.79 0.30

4 Unmod 2454 1056 1.87 0.30

5 Mod 3079 1006 1.81 0.26

5 Unmod 2123 992 1.90 0.26

6 Mod 3161 602 1.77 0.296 Unmod 2678 798 1.94 0.21

7 Mod 2681 575 1.83 0.33

7 Unmod 1952 757 1.90 0.30

8 Mod 3216 851 1.78 0.19

8 Unmod 2029 1051 1.87 0.20

9 Mod 2695 996 1.76 0.32

9 Unmod 1925 935 1.90 0.29

Mesoscale ModelsMesoscale Models

Here at NWSO TAE, we use the non-hydrostatic Here at NWSO TAE, we use the non-hydrostatic version of the PSU/NCAR MM5 run on an HP-755version of the PSU/NCAR MM5 run on an HP-755

Initialize with the eta/meso eta for course grid Initialize with the eta/meso eta for course grid boundary conditionsboundary conditions

Run operationally twice a day at 15 km Run operationally twice a day at 15 km horizontal resolutionhorizontal resolution

Output from operational runs can be viewed on Output from operational runs can be viewed on the web at: the web at: http://www.http://www.nwsnws..fsufsu..eduedu//tlhtlh/mm5/mm5

Idealized runsIdealized runs– Run nested at 15/5 km horizontal resolutionRun nested at 15/5 km horizontal resolution– Initialized with low level flows from climatologyInitialized with low level flows from climatology– Initialized with 12 UTC unmodified soundings from Initialized with 12 UTC unmodified soundings from

climatologyclimatology

Climatology DesignClimatology Design

Choose type or typesChoose type or types

Choose channels and productsChoose channels and products

Choose synoptic flow stratificationChoose synoptic flow stratification

Integrate complementary climatologies and Integrate complementary climatologies and mesoscale modelingmesoscale modeling

Test in forecast situationsTest in forecast situations

Suggested ReadingSuggested Reading Klitch, M.A., J.F. Weaver, F.P. Kelly, and T.H. Vonder Haar, 1985: Klitch, M.A., J.F. Weaver, F.P. Kelly, and T.H. Vonder Haar, 1985:

Convective cloud climatologies constructed from satellite imagery. Convective cloud climatologies constructed from satellite imagery. Mon. Wea. Rev.,Mon. Wea. Rev., 113113, 326-337., 326-337.

Gibson, H.M., and T.H. Vonder Haar, 1990: Cloud and convection Gibson, H.M., and T.H. Vonder Haar, 1990: Cloud and convection frequency over the southeast United States as related to small frequency over the southeast United States as related to small scale geographic features. scale geographic features. Mon. Wea. Rev., Mon. Wea. Rev., 118,118, 2215-2227. 2215-2227.

Hall, T.J., D.L. Reinke, and T.H. Vonder Haar, 1998: Forecasting Hall, T.J., D.L. Reinke, and T.H. Vonder Haar, 1998: Forecasting applications of high resolution satellite cloud composite applications of high resolution satellite cloud composite climatologies. climatologies. Wea. and For., Wea. and For., 13,13, 16-23. 16-23.

Gould, K.J., and H.E. Fuelberg, 1996: The use of GOES-8 imagery Gould, K.J., and H.E. Fuelberg, 1996: The use of GOES-8 imagery and RAMSDIS to develop a sea breeze climatology over the Florida and RAMSDIS to develop a sea breeze climatology over the Florida Panhandle. Preprints, 8th Conf. On Satellite Meteorology and Panhandle. Preprints, 8th Conf. On Satellite Meteorology and Oceanography, 100-104.Oceanography, 100-104.

QuestionsQuestions 1. Weighing all of the advantages and disadvantages, which 1. Weighing all of the advantages and disadvantages, which

type of mesoscale cloud climatology is probably the best?type of mesoscale cloud climatology is probably the best?

– a. Generala. General

– b. Seasonalb. Seasonal

– c. Monthlyc. Monthly

– d. Seasonal Phenomenon Drivend. Seasonal Phenomenon Driven

– e. Monthly Phenomenon Drivene. Monthly Phenomenon Driven

2. Name three examples of a phenomenon driven MCC2. Name three examples of a phenomenon driven MCC

3. If performing a phenomenon driven MCC for wild 3. If performing a phenomenon driven MCC for wild fires/prescribed burns, what two GOES 8/10 channels would fires/prescribed burns, what two GOES 8/10 channels would be best to use?be best to use?

– a. Water vapor/ 3.9 micron IRa. Water vapor/ 3.9 micron IR

– b. 10.7 micron IR/ visibleb. 10.7 micron IR/ visible

– c. Fog product/ 10.7 micron IRc. Fog product/ 10.7 micron IR

– d. 3.9 micron IR/ visibled. 3.9 micron IR/ visible

– e. Visible/ water vapore. Visible/ water vapor

4. In developing a stratification scheme based on the low level 4. In developing a stratification scheme based on the low level synoptic flow, why should the surface wind generally be synoptic flow, why should the surface wind generally be avoided?avoided?

5. How would an MCC be used as an aid in the following NWS 5. How would an MCC be used as an aid in the following NWS forecast products:forecast products:

– a. Aviation forecasts?a. Aviation forecasts?

– b. Zone forecasts?b. Zone forecasts?

– c. Marine forecasts?c. Marine forecasts?

6. Name three complementary mesoscale climatologies one 6. Name three complementary mesoscale climatologies one might use in conjunction with an MCC.might use in conjunction with an MCC.

7. How could precipitation climatologies be used when 7. How could precipitation climatologies be used when composing a zone forecast?composing a zone forecast?

8. Besides a sea breeze climatology, what other phenomenon 8. Besides a sea breeze climatology, what other phenomenon driven MCC might be served well by a thermodynamic driven MCC might be served well by a thermodynamic climatology?climatology?

AnswersAnswers 1. d. A monthly phenomenon driven MCC would also be good, 1. d. A monthly phenomenon driven MCC would also be good,

but it would take much longer to build a significant data base.but it would take much longer to build a significant data base.

2. Sea breeze, fog/sea fog/stratus, orographic, fire2. Sea breeze, fog/sea fog/stratus, orographic, fire

3. d.3. d.

4. The surface wind should generally be avoided since it may 4. The surface wind should generally be avoided since it may contain mesoscale contamination. For example, in determining contain mesoscale contamination. For example, in determining the synoptic flow for a sea breeze climatology, the surface wind the synoptic flow for a sea breeze climatology, the surface wind may contain a component of the land or sea breeze which is non-may contain a component of the land or sea breeze which is non-representative of the synoptic flow.representative of the synoptic flow.

5. a. An MCC will give an excellent first guess of the timing and 5. a. An MCC will give an excellent first guess of the timing and preferred locations of the development and dissipation of clouds preferred locations of the development and dissipation of clouds and fog. and fog. – b. As in a., in addition to, likely areas of precipitation and convective b. As in a., in addition to, likely areas of precipitation and convective

development. This may help determine zone grouping.development. This may help determine zone grouping.– c. Similar to a., with emphasis on sea fog and stratus.c. Similar to a., with emphasis on sea fog and stratus.

Answers (cont.)Answers (cont.) 6. Precipitation, POP, Radar (HDP), Lightning, Thermodynamic.6. Precipitation, POP, Radar (HDP), Lightning, Thermodynamic.

7. Precipitation climatologies could be used as an excellent first 7. Precipitation climatologies could be used as an excellent first guess for grouping zones by probability of precipitation (POP), in guess for grouping zones by probability of precipitation (POP), in addition to a rough first guess of QPF. Also, the HDP data can be addition to a rough first guess of QPF. Also, the HDP data can be used as a verification tool for both zone and QPF forecasts.used as a verification tool for both zone and QPF forecasts.

8. A fog/sea fog/stratus MCC should be well served by a 8. A fog/sea fog/stratus MCC should be well served by a thermodynamic climatology. The mean sounding structure thermodynamic climatology. The mean sounding structure should be a good indicator of days with or without fog/sea should be a good indicator of days with or without fog/sea fog/stratus. In fact, with a well stratified thermodynamic fog/stratus. In fact, with a well stratified thermodynamic climatology, one might even be able to delineate fog/sea fog vs. climatology, one might even be able to delineate fog/sea fog vs. stratus. (one of the more challenging forecast problems) stratus. (one of the more challenging forecast problems)