research methods for working with helsinki testbed data

Research Methods for Research Methods for Working with Helsinki Testbed DataWorking with Helsinki Testbed Data

Including Class Project Ideas!!!!Including Class Project Ideas!!!!

Synoptic and Mesoscale AnalysisSynoptic and Mesoscale Analysis

Describe weather patterns, structures, Describe weather patterns, structures, evolutions.evolutions.

Get at processes responsible for structures and Get at processes responsible for structures and observed weather. observed weather.

Nonclassical Cold-Frontal Structure Nonclassical Cold-Frontal Structure Caused by Dry Subcloud Air in Caused by Dry Subcloud Air in

Northern Utah during IPEXNorthern Utah during IPEX

David M. SchultzDavid M. Schultz and Robert J. Trapp and Robert J. Trapp

CIMMS and NSSL, Norman, OklahomaCIMMS and NSSL, Norman, Oklahoma

October 2003 October 2003 Monthly Weather ReviewMonthly Weather Review

and Manuscript in Preparationand Manuscript in Preparation

Map of UtahMap of Utah

Oasis(NSSL4)

•

NSSL4 time NSSL4 time seriesseries

• temperature drops nearly 8°C in 8 minutes

• pressure rises 20 minutes before temperature drops

• wind changes direction in concert with pressure rise

• RH increases after frontal passage

• RH decreases and temperature rises two hours after frontal passage

North to south station time North to south station time seriesseries

rate of temperature drop decreases as front moves south, although total temperature drop is nearly constant

PVUCFO

SNH

IPX2

IPX8

Snowbasin time seriesSnowbasin time series

temperature drop occurs earlier with heightpostfrontal temperature rise decreases with height

Temp change as a function of Temp change as a function of heightheight

Precipitation decreases linearly with height below cloud base.

orographicallyunfavorable orographically

favorable

Precipitation is nearly constant above cloud base.Orographic influences are greatest above cloud base.

SummarySummary Forward-sloping cloud with mammatus Forward-sloping cloud with mammatus

and superadiabatic layer underneath and superadiabatic layer underneath indicates importance of subcloud indicates importance of subcloud sublimation.sublimation.– Cooling aloft precedes that at surfaceCooling aloft precedes that at surface– Pressure trough precedes front at Pressure trough precedes front at

surfacesurface– Destabilization of prefrontal environmentDestabilization of prefrontal environment– Dry subcloud air promotes strong Dry subcloud air promotes strong

coolingcooling

Types of Potential Testbed ProjectsTypes of Potential Testbed Projects Case study of sea-breezeCase study of sea-breeze Case study of fronts or severe weatherCase study of fronts or severe weather Case study of air-quality episodeCase study of air-quality episode

Climatology and CompositesClimatology and Composites(and a little bit of statistics)(and a little bit of statistics)

Describe long-term weather (climate) patterns.Describe long-term weather (climate) patterns.

Composites (average) represent the typical pattern Composites (average) represent the typical pattern associated with the weather phenomenon in questionassociated with the weather phenomenon in question

Regression models are used to predict relevant Regression models are used to predict relevant observational quantities for forecasting. observational quantities for forecasting.

Intraseasonal Variability of the North American Monsoon in Arizona

Pamela Heinselman

Dissertation Seminar14 October 2003

(Will it Boomer Sooner or Later?)

Forecast Challenges:

•Where will storms initiate over elevated terrain?

•Will storms develop over the mountains only, or over Phoenix as well?

Central Mountains

Bursts & Breaks

Today’s weather

GoalsGoalsAdvance our understanding of the Advance our understanding of the intraseasonal variability of diurnal storm intraseasonal variability of diurnal storm development and atmospheric environment in development and atmospheric environment in Arizona during the NAMArizona during the NAM

– 1. Do storms tend to initiate and evolve repeatedly 1. Do storms tend to initiate and evolve repeatedly over similar regions?over similar regions?

– 2. What environmental conditions are related to 2. What environmental conditions are related to diurnal storm development? diurnal storm development?

– 3. How do storm development, Phoenix 3. How do storm development, Phoenix soundings, and synoptic-scale flow evolve on a soundings, and synoptic-scale flow evolve on a daily basis? daily basis?

Data: July – August 1997 & 1999

Central Mountains

Radar

Rawinsonde

1. Do storms tend to initiate and evolve 1. Do storms tend to initiate and evolve repeatedly over similar regions? repeatedly over similar regions?

Composite radar reflectivity mosaicsComposite radar reflectivity mosaics– JulyJulyAugust 1997 & 1999 WSR-88D reflectivity data from August 1997 & 1999 WSR-88D reflectivity data from

Phoenix and Flagstaff mapped to 1-km Cartesian grid every Phoenix and Flagstaff mapped to 1-km Cartesian grid every 10 min 10 min ( 112/124 days)( 112/124 days)

1-km digitized terrain data1-km digitized terrain data Variability in storm development is investigated subjectively by Variability in storm development is investigated subjectively by

observing the diurnal evolution of hourly composite radar observing the diurnal evolution of hourly composite radar reflectivity mosaicsreflectivity mosaics– Illustrate similarity in regions where storms tend to develop Illustrate similarity in regions where storms tend to develop

by calculating diurnal relative frequencies of radar reflectivity by calculating diurnal relative frequencies of radar reflectivity 25 dB 25 dBZ Z for days comprising each patternfor days comprising each pattern

1. Do storms tend to initiate and evolve 1. Do storms tend to initiate and evolve repeatedly over similar regions? repeatedly over similar regions?

YES!YES!– Reflectivity Regimes include:Reflectivity Regimes include:

Dry (DR)Dry (DR) Eastern Mountain (EMR)Eastern Mountain (EMR) CentralCentral––Eastern Mountain (CEMR)Eastern Mountain (CEMR) CentralCentral––Eastern and Sonoran Desert (CEMSR)Eastern and Sonoran Desert (CEMSR) Non-Diurnal (NDR)Non-Diurnal (NDR)

– North-moving (11 events or 46%)North-moving (11 events or 46%)– East-moving (7 events or 29%)East-moving (7 events or 29%)– West-moving (6 events or 25%)West-moving (6 events or 25%)

Unclassified (UNC)Unclassified (UNC)

Eastern MountainEastern MountainRelative frequency of reflectivity 25 dBZ

N=11 or 9 %

%

1820 UTC (1113 LST) 2200 UTC (1517 LST)

0204 UTC (1921 LST) 0608 UTC (23 01 LST)

July−August 1997 & 1999

CentralCentral––Eastern MountainEastern MountainRelative frequency of reflectivity 25 dBZ

N=39 or 31.5 %

1820 UTC (1113 LST) 2200 UTC (1517 LST)

0204 UTC (1921 LST) 0608 UTC (23 01 LST)

%


CentralCentral––Eastern Mountain & SonoranEastern Mountain & SonoranRelative frequency of reflectivity 25 dBZ

N=20 or 16 %

%

1820 UTC (1113 LST) 2200 UTC (1517 LST)

0204 UTC (1921 LST) 0608 UTC (23 01 LST)


Non-DiurnalNon-DiurnalRelative frequency of reflectivity 25 dBZ

N=24 or 16 %

%

1820 UTC (1113 LST) 2200 UTC (1517 LST)

0204 UTC (1921 LST) 0608 UTC (23 01 LST)


2. What synoptic-scale conditions are 2. What synoptic-scale conditions are related to each reflectivity regime? related to each reflectivity regime?

NEXT:

• Composite 500 mb maps

Dry RegimeDry Regime

500-mb Geopotential Height 500-mb Specific Humidity

• Composite maps from CDC website, constructed using NCEP reanalysis data (N=13)

• Pattern similar to breaks and pre-monsoon conditions

Eastern Mountain RegimeEastern Mountain Regime

500-mb Geopotential Height 500-mb Specific Humidity


• Pattern similar to monsoon boundary (Adang and Gall 1989)

CentralCentral––Eastern Mountain RegimeEastern Mountain Regime

500-mb Geopotential Heights 500-mb Specific Humidity


• Westward expansion of subtropical anticyclone / meridional moist axis

CentralCentral––Eastern Mountain & Sonoran RegimeEastern Mountain & Sonoran Regime



• Subtropical ridge builds northwestward southeasterly flow

• More moist at 500 mb

Non-Diurnal RegimeNon-Diurnal Regime



• Numerous shortwave troughs, not seen in composites

• Meridional moist axis extends farther west and north

Synoptic and Mesoscale Influences on West Texas Dryline Development

and Associated Convection

Christopher WeissTexas Tech University, Lubbock, TX

David SchultzNational Severe Storm Laboratory/CIMMS

Norman, OK

2.6

West Texas MesonetWest Texas Mesonet West Texas Mesonet (WTM) has been steadily growing West Texas Mesonet (WTM) has been steadily growing

since its inception in 2002.since its inception in 2002.

As of early October, a total of 49 stations are As of early October, a total of 49 stations are operational across the Texas Panhandle.operational across the Texas Panhandle.

Now possible to perform multi-year climatological Now possible to perform multi-year climatological analysis of features routinely observed in West Texas, analysis of features routinely observed in West Texas, including drylines.including drylines.

Our Understanding of Our Understanding of Dryline Structure and PropagationDryline Structure and Propagation

VerticalMixing of

Heat/Momentum +

Terrain Slope

Synoptic-Scale Forcing

Land-Use / Soil Moisture

Gradients

“Internal” Solenoidal Circulations

GOALS: GOALS: To resolve the To resolve the

dependency of dependency of dryline intensity dryline intensity on the background on the background synoptic patternsynoptic pattern

To identify To identify pertinent synoptic pertinent synoptic and mesoscale and mesoscale forcing factors for forcing factors for dryline convection dryline convection initiation and initiation and modemode

Our Understanding of Our Understanding of Dryline Structure and PropagationDryline Structure and Propagation

Synoptic-Scale Forcing

Dryline Case SelectionDryline Case Selection

A dryline case satisfied the following criteria:A dryline case satisfied the following criteria:

An eastward directed dewpoint-gradient (An eastward directed dewpoint-gradient (TTdd))at 1800 at 1800 LTLT

TTd d exceeded 1 exceeded 1 ooC, corresponding to a constant mixing C, corresponding to a constant mixing ratio at stations MORT and PADU (different elevation)ratio at stations MORT and PADU (different elevation)

No contribution to No contribution to TTdd from convective outflow or a from convective outflow or a frontal boundaryfrontal boundary

TTd d increased between 0700 LT and 1800 LTincreased between 0700 LT and 1800 LT A deceleration in eastward propagation / acceleration A deceleration in eastward propagation / acceleration

of westward propagation was evident near and after of westward propagation was evident near and after 1800 LT1800 LT

Most of the dewpoint gradient (per regional Most of the dewpoint gradient (per regional observations) was contained within the WTM domain observations) was contained within the WTM domain (subjective)(subjective)

MORTPADU

Period of studyApril-June 2004-2005

DomainWTM

MethodMethod 64 dryline cases identified 64 dryline cases identified Cases ranked by intensity (Cases ranked by intensity (TTdd)) Upper quartile of cases classified as Upper quartile of cases classified as

“strong” (16)“strong” (16) Lower quartile of cases classified as Lower quartile of cases classified as

“weak” (16)“weak” (16) Synoptic composites generated using Synoptic composites generated using

data from the NCAR/NCEP Reanalysis data from the NCAR/NCEP Reanalysis

(available at http://www.cdc.noaa.gov)(available at http://www.cdc.noaa.gov)

Dryline Intensity vs. ConfluenceDryline Intensity vs. Confluence(all cases, WTM domain scale)(all cases, WTM domain scale)

(Schultz et al. 2006)

• Clear correlation between WTM-scale dryline intensity and confluence• However, significant outliers exist. Conclusion:

Confluence within scale of WTM domain width Variations in duration/strength of confluence Other processes involved in forcing

500 mb Geopotential Height500 mb Geopotential Height

STRONG WEAK


Sea Level PressureSea Level Pressure

STRONG WEAK


Dryline ConvectionDryline Convection Logistic regression (stepwise selection) Logistic regression (stepwise selection)

employed to find pertinent forcing foremployed to find pertinent forcing for

convection convection initiation initiation and and mode.mode.

Potential regressors collected from:Potential regressors collected from:

WTM

TTdd WTM-wide WTM-wide dewpoint dewpoint difference (1800 difference (1800 LT)LT)

TTd,mad,ma

xx

Maximum Maximum dewpoint dewpoint difference difference between adjacent between adjacent east-west WTM east-west WTM station (1800 LT)station (1800 LT)

uu WTM-wide zonal WTM-wide zonal wind component wind component difference (1800 difference (1800 LT)LT)

MORT PADU

Logit Function(Ryan 1997)

More Potential RegressorsMore Potential RegressorsNCEP/NCAR Reanalysis

qq850,700,500850,700,500 Specific humidity at level XXX Specific humidity at level XXX hPa at 0000 UTC (qhPa at 0000 UTC (q850850 not used not used

for location “W”)for location “W”)

TT850,700,500850,700,500 Temperature at level XXX hPa at Temperature at level XXX hPa at 0000 UTC0000 UTC

UU700,500700,500 Zonal wind component at level Zonal wind component at level XXX hPa at 0000 UTCXXX hPa at 0000 UTC

TT850-500850-500 Temperature lapse rate from 850 Temperature lapse rate from 850 to 500 hPa at 0000 UTCto 500 hPa at 0000 UTC



Gridpoint Locations

WTM Domain

Regression ModelsRegression Models(12 total, 6 at position “E”, 6 at position “W”)(12 total, 6 at position “E”, 6 at position “W”)

CuCu For all dryline cases, any moist convection For all dryline cases, any moist convection along the drylinealong the dryline

CbCb For all dryline cases, any cumulonimbus For all dryline cases, any cumulonimbus (Cb) development along the dryline(Cb) development along the dryline

SevereSevere For all dryline cases, any Cb development For all dryline cases, any Cb development with associated non-tornadic severe with associated non-tornadic severe weather reports in the WTM domainweather reports in the WTM domain

TornadoTornado For all dryline cases, any Cb development For all dryline cases, any Cb development with at least one tornado report in the with at least one tornado report in the WTM domainWTM domain

Severe | CbSevere | Cb For all dryline Cb cases, any severe For all dryline Cb cases, any severe weather reports in the WTM domainweather reports in the WTM domain

Tornado | Tornado | CbCb

For all dryline Cb cases, any tornado For all dryline Cb cases, any tornado reports in the WTM domainreports in the WTM domain

ResultsResultsModelModel LocationLocation Predictors (in order of Predictors (in order of

selection)selection)Negative coefficients in italicsNegative coefficients in italics

CuCu WW qq700700, , TTdd, , TT500500

CbCb WW TT850850–T–T500500, q, q700, 700, TTd, d, TT700700

SevereSevere WW TTdd, q, q700700, , TT500500

TornadoTornado WW TTd,maxd,max, U, U500500, T, T850850

Severe|CbSevere|Cb WW TTdd

Tornado|CbTornado|Cb WW UU500500, T, T850, 850, U, U, TTdd

CuCu EE qq700700, , TT500500, q, q850850

CbCb EE qq700, 700, TT850850–T–T500500

SevereSevere EE TTdd, T, T700700–T–T500500, q, q700700

TornadoTornado EE TTd,maxd,max, U, U500500, T, T850850–T–T500500

Severe|CbSevere|Cb EE TT700700–T–T500500, , TT850850–T–T500500

Tornado|CbTornado|Cb EE UU500500, T, T700700, , U, U, TTdd



CuCu WW qq700700, , TTdd, , TT500500






CuCu EE qq700700, , TT500500, q, q850850

CbCb EE qq700, 700, TT850850–T–T500500





1) As expected, lower tropospheric specific humidity is a prominent factor in generation of moist convection.



CuCu WW qq700700, , TTdd, , TT500500






CuCu EE qq700700, , TT500500, q, q850850

CbCb EE qq700, 700, TT850850–T–T500500





2) As expected, stronger zonal momentum figures prominently in the occurrence of dryline-associated tornadic storms.



CuCu WW qq700700, , TTdd, , TT500500






CuCu EE qq700700, , TT500500, q, q850850

CbCb EE qq700, 700, TT850850–T–T500500





3) Generally, large low-mid tropospheric lapse rates favor LFC attainment near initiation point, and severity of convective development downstream.



CuCu WW qq700700, , TTdd, , TT500500






CuCu EE qq700700, , TT500500, q, q850850

CbCb EE qq700, 700, TT850850–T–T500500





4) Deeper-layer (T850-T500) and shallower-layer (T700-T500) lapse rates do explain separate variance occasionally (Griesinger and Weiss, 1.5).



CuCu WW qq700700, , TTdd, , TT500500






CuCu EE qq700700, , TT500500, q, q850850

CbCb EE qq700, 700, TT850850–T–T500500





5) Dryline “strength” significant in determining intensity of resultant convection.

Primary ConclusionsPrimary Conclusions A continuum of dryline events exists – application of A continuum of dryline events exists – application of

arbitrary specific humidity gradient thresholds arbitrary specific humidity gradient thresholds removes weak dryline cases.removes weak dryline cases.

Background synoptic pattern influences dryline Background synoptic pattern influences dryline intensity. intensity. – The Rocky Mountain lee trough, specifically, is shown to be The Rocky Mountain lee trough, specifically, is shown to be

present for even the weakest of dryline events.present for even the weakest of dryline events.– More confluent drylines tend to be more intense, though More confluent drylines tend to be more intense, though

significant outliers existsignificant outliers exist..

Synoptic pattern and dryline characteristics Synoptic pattern and dryline characteristics influence initiation and severity of convection influence initiation and severity of convection (continuing investigation).(continuing investigation).– Dryline intensity is a significant forcing factor for severity of Dryline intensity is a significant forcing factor for severity of

subsequent convection.subsequent convection.– Low to mid-tropospheric lapse rates near dryline are significant Low to mid-tropospheric lapse rates near dryline are significant

for initiation of deep moist convection; same lapse rates east of for initiation of deep moist convection; same lapse rates east of the dryline significant for severity of convection downstream.the dryline significant for severity of convection downstream.

– 850-500 mb and 700-500 mb lapse rate can occasionally explain 850-500 mb and 700-500 mb lapse rate can occasionally explain separate variance (where coefficients are opposite in sign). separate variance (where coefficients are opposite in sign).

Types of Potential Testbed ProjectsTypes of Potential Testbed Projects Composite sea-breeze events: events that Composite sea-breeze events: events that

move onshore vs. quasistationary eventsmove onshore vs. quasistationary events Composite good/bad air-quality episodesComposite good/bad air-quality episodes Strong versus weak inversionsStrong versus weak inversions Long-lived inversions or low-visibility casesLong-lived inversions or low-visibility cases Can statistical prediction equations be Can statistical prediction equations be

developed given high-resolution observations developed given high-resolution observations (e.g., experience at the 2002 Winter Olympic (e.g., experience at the 2002 Winter Olympic Games suggests you don’t need a lot of data)?Games suggests you don’t need a lot of data)?

LinksLinks http://www.cdc.noaa.gov/Composites/Dayhttp://www.cdc.noaa.gov/Composites/Day http://www.cdc.noaa.gov/Composites/Hourhttp://www.cdc.noaa.gov/Composites/Hour http://www.cdc.noaa.gov/Composites/NSSL/Dayhttp://www.cdc.noaa.gov/Composites/NSSL/Day

http://www.cdc.noaa.gov/Composites/Day

http://www.cdc.noaa.gov/Composites/Hour

Verification of Numerical Models, Verification of Numerical Models, Quality Control, and Instrument Quality Control, and Instrument

CalibrationCalibration

Types of Potential Testbed ProjectsTypes of Potential Testbed Projects What are characteristic errors associated with certain What are characteristic errors associated with certain

stations (stable layers near surface, precipitation)?stations (stable layers near surface, precipitation)? What are the NWP errors associated with a given case?What are the NWP errors associated with a given case? Instrument cross-comparison (particularly for remote-Instrument cross-comparison (particularly for remote-

sensing data)sensing data) Can the “shelter effect” be quantified?Can the “shelter effect” be quantified? What is the effect of the mast on temperatures at the same What is the effect of the mast on temperatures at the same

level?level? How good is the WXT for hail or drop-size distributions?How good is the WXT for hail or drop-size distributions? Automatic detection of weather phenomenaAutomatic detection of weather phenomena Advancing QC methodsAdvancing QC methods

Societal, Economic, and Business Societal, Economic, and Business ImpactsImpacts

Roebber and Bosart (1998): The complex Roebber and Bosart (1998): The complex relationship between forecast skill and forecast value: relationship between forecast skill and forecast value: A real-world analysis. A real-world analysis. Weather and ForecastingWeather and Forecasting, , 11,11, 544–559.544–559.

aa bb

cc dd

Adverseweather

No adverseweather

Protect

Do Not Protect

Cost–Loss Ratio:p(event) >=(b–d)/[(b–d)+(c–a)]then protect

Types of Potential Testbed ProjectsTypes of Potential Testbed Projects How are business decisions by a certain company or a How are business decisions by a certain company or a

business sector affected (or could be affected) by business sector affected (or could be affected) by access to Testbed data?access to Testbed data?– Construction: what kind of information do they need and Construction: what kind of information do they need and

with what specificity?with what specificity?– Calculate the cost–loss ratio for a specific business Calculate the cost–loss ratio for a specific business

interest, for Testbed data and traditional data.interest, for Testbed data and traditional data. What is the value of high-resolution temperature/wind What is the value of high-resolution temperature/wind

data for specific users (e.g., temperatures for electric data for specific users (e.g., temperatures for electric companies at substations, as opposed to airports)? companies at substations, as opposed to airports)?

A business prospectus for a specific company using A business prospectus for a specific company using Testbed data as an example.Testbed data as an example.

Health and weather/climate studies (hospital and Health and weather/climate studies (hospital and mortality statistics), weather event leads to more mortality statistics), weather event leads to more hospital visits in some part of Helsinki?hospital visits in some part of Helsinki?

research methods for working with helsinki testbed data

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