research methods for working with helsinki testbed data n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Research Methods for Working with Helsinki Testbed Data PowerPoint Presentation
Download Presentation
Research Methods for Working with Helsinki Testbed Data

Loading in 2 Seconds...

play fullscreen
1 / 58

Research Methods for Working with Helsinki Testbed Data - PowerPoint PPT Presentation


  • 111 Views
  • Uploaded on

Research Methods for Working with Helsinki Testbed Data. Including Class Project Ideas!!!!. Synoptic and Mesoscale Analysis. Describe weather patterns, structures, evolutions. Get at processes responsible for structures and observed weather.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Research Methods for Working with Helsinki Testbed Data' - penda


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
synoptic and mesoscale analysis

Synoptic and Mesoscale Analysis

Describe weather patterns, structures, evolutions.

Get at processes responsible for structures and observed weather.

nonclassical cold frontal structure caused by dry subcloud air in northern utah during ipex

Nonclassical Cold-Frontal Structure Caused by Dry Subcloud Air in Northern Utah during IPEX

David M. Schultz and Robert J. Trapp

CIMMS and NSSL, Norman, Oklahoma

October 2003 Monthly Weather Review

and Manuscript in Preparation

map of utah

Oasis

(NSSL4)

Map of Utah
nssl4 time series
NSSL4 time series

• 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 series

IPX8

North to south station time series

IPX2

SNH

CFO

PVU

rate of temperature drop decreases as front moves south,

although total temperature drop is nearly constant

snowbasin time series
Snowbasin time series

temperature drop occurs earlier with height

postfrontal temperature rise decreases with height

slide10

orographically

unfavorable

orographically

favorable

Precipitation decreases linearly with height below cloud base.

Precipitation is nearly constant above cloud base.

Orographic influences are greatest above cloud base.

summary
Summary
  • Forward-sloping cloud with mammatus and superadiabatic layer underneath indicates importance of subcloud sublimation.
    • Cooling aloft precedes that at surface
    • Pressure trough precedes front at surface
    • Destabilization of prefrontal environment
    • Dry subcloud air promotes strong cooling
types of potential testbed projects
Types of Potential Testbed Projects
  • Case study of sea-breeze
  • Case study of fronts or severe weather
  • Case study of air-quality episode
climatology and composites and a little bit of statistics

Climatology and Composites(and a little bit of statistics)

Describe long-term weather (climate) patterns.

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

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

slide16

Intraseasonal Variability of the North American Monsoon in Arizona

(Will it Boomer Sooner or Later?)

Pamela Heinselman

Dissertation Seminar

14 October 2003

slide17

Bursts & Breaks

Today’s weather

  • Forecast Challenges:
    • Where will storms initiate over elevated terrain?
    • Will storms develop over the mountains only, or over Phoenix as well?

Central Mountains

goals
Goals

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

  • 1. Do storms tend to initiate and evolve repeatedly over similar regions?
  • 2. What environmental conditions are related to diurnal storm development?
  • 3. How do storm development, Phoenix soundings, and synoptic-scale flow evolve on a daily basis?
slide19

Data: July – August 1997 & 1999

Central Mountains

Radar

Rawinsonde

1 do storms tend to initiate and evolve repeatedly over similar regions
1. Do storms tend to initiate and evolve repeatedly over similar regions?
  • Composite radar reflectivity mosaics
    • JulyAugust 1997 & 1999 WSR-88D reflectivity data from Phoenix and Flagstaff mapped to 1-km Cartesian grid every 10 min ( 112/124 days)
  • 1-km digitized terrain data
  • Variability in storm development is investigated subjectively by observing the diurnal evolution of hourly composite radar reflectivity mosaics
    • Illustrate similarity in regions where storms tend to develop by calculating diurnal relative frequencies of radar reflectivity  25 dBZ for days comprising each pattern
1 do storms tend to initiate and evolve repeatedly over similar regions1
1. Do storms tend to initiate and evolve repeatedly over similar regions?
  • YES!
    • Reflectivity Regimes include:
      • Dry (DR)
      • Eastern Mountain (EMR)
      • Central–Eastern Mountain (CEMR)
      • Central–Eastern and Sonoran Desert (CEMSR)
      • Non-Diurnal (NDR)
        • North-moving (11 events or 46%)
        • East-moving (7 events or 29%)
        • West-moving (6 events or 25%)
      • Unclassified (UNC)
eastern mountain
Eastern Mountain

Relative frequency of reflectivity  25 dBZ

1820 UTC (1113 LST)

2200 UTC (1517 LST)

N=11 or 9 %

%

0204 UTC (1921 LST)

0608 UTC (23 01 LST)

July−August 1997 & 1999

central eastern mountain
Central–Eastern Mountain

Relative frequency of reflectivity  25 dBZ

1820 UTC (1113 LST)

2200 UTC (1517 LST)

N=39 or 31.5 %

%

0204 UTC (1921 LST)

0608 UTC (23 01 LST)

July−August 1997 & 1999

central eastern mountain sonoran

%

Central–Eastern Mountain & Sonoran

Relative frequency of reflectivity  25 dBZ

1820 UTC (1113 LST)

2200 UTC (1517 LST)

N=20 or 16 %

0204 UTC (1921 LST)

0608 UTC (23 01 LST)

July−August 1997 & 1999

non diurnal

%

Non-Diurnal

Relative frequency of reflectivity  25 dBZ

1820 UTC (1113 LST)

2200 UTC (1517 LST)

N=24 or 16 %

0204 UTC (1921 LST)

0608 UTC (23 01 LST)

July−August 1997 & 1999

2 what synoptic scale conditions are related to each reflectivity regime
2. What synoptic-scale conditions are related to each reflectivity regime?
  • NEXT:
  • Composite 500 mb maps
dry regime
Dry 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 regime
Eastern Mountain Regime

500-mb Geopotential Height

500-mb Specific Humidity

  • Composite maps from CDC website, constructed using NCEP reanalysis data (N=11)
  • Pattern similar to monsoon boundary (Adang and Gall 1989)
central eastern mountain regime
Central–Eastern Mountain Regime

500-mb Geopotential Heights

500-mb Specific Humidity

  • Composite maps from CDC website, constructed using NCEP reanalysis data (N=39)
  • Westward expansion of subtropical anticyclone / meridional moist axis
central eastern mountain sonoran regime
Central–Eastern Mountain & Sonoran Regime

500-mb Geopotential Heights

500-mb Specific Humidity

  • Composite maps from CDC website, constructed using NCEP reanalysis data (N=20)
  • Subtropical ridge builds northwestward southeasterly flow
  • More moist at 500 mb
non diurnal regime
Non-Diurnal Regime

500-mb Geopotential Heights

500-mb Specific Humidity

  • Composite maps from CDC website, constructed using NCEP reanalysis data (N=24)
  • Numerous shortwave troughs, not seen in composites
  • Meridional moist axis extends farther west and north
slide32

2.6

Synoptic and Mesoscale Influences on

West Texas Dryline Development

and Associated Convection

Christopher Weiss

Texas Tech University, Lubbock, TX

David Schultz

National Severe Storm Laboratory/CIMMS

Norman, OK

west texas mesonet
West Texas Mesonet
  • West Texas Mesonet (WTM) has been steadily growing since its inception in 2002.
  • As of early October, a total of 49 stations are operational across the Texas Panhandle.
  • Now possible to perform multi-year climatological analysis of features routinely observed in West Texas, including drylines.
our understanding of dryline structure and propagation
Our Understanding of Dryline Structure and Propagation

Vertical

Mixing of Heat/Momentum +

Terrain Slope

Synoptic-Scale

Forcing

Land-Use /

Soil Moisture

Gradients

“Internal”

Solenoidal

Circulations

our understanding of dryline structure and propagation1
Our Understanding of Dryline Structure and Propagation

Vertical

Mixing of Heat/Momentum +

Terrain Slope

Synoptic-Scale

Forcing

Land-Use /

Soil Moisture

Gradients

“Internal”

Solenoidal

Circulations

slide36
GOALS:

To resolve the dependency of dryline intensity on the background synoptic pattern

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

Our Understanding of Dryline Structure and Propagation

Synoptic-Scale

Forcing

dryline case selection
Dryline Case Selection

Period of study

April-June 2004-2005

Domain

WTM

A dryline case satisfied the following criteria:

  • An eastward directed dewpoint-gradient (DTd)at 1800 LT
  • DTd exceeded 1 oC, corresponding to a constant mixing ratio at stations MORT and PADU (different elevation)
  • No contribution to DTd from convective outflow or a frontal boundary
  • DTd increased between 0700 LT and 1800 LT
  • A deceleration in eastward propagation / acceleration of westward propagation was evident near and after 1800 LT
  • Most of the dewpoint gradient (per regional observations) was contained within the WTM domain (subjective)

PADU

MORT

method
Method
  • 64 dryline cases identified
  • Cases ranked by intensity (DTd)
  • Upper quartile of cases classified as “strong” (16)
  • Lower quartile of cases classified as “weak” (16)
  • Synoptic composites generated using data from the NCAR/NCEP Reanalysis

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

dryline intensity vs confluence all cases wtm domain scale
Dryline Intensity vs. Confluence(all cases, WTM domain scale)
  • 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

(Schultz et al. 2006)

slide40

500 mb Geopotential Height

WEAK

STRONG

(Schultz et al. 2006)

slide41

Sea Level Pressure

WEAK

STRONG

(Schultz et al. 2006)

dryline convection
Dryline Convection
  • Logistic regression (stepwise selection)

employed to find pertinent forcing for

convection initiation and mode.

  • Potential regressors collected from:

Logit Function

(Ryan 1997)

WTM

PADU

MORT

more potential regressors
More Potential Regressors

NCEP/NCAR Reanalysis

WTM Domain

Gridpoint Locations

results1
Results
  • As expected, lower tropospheric specific humidity is a prominent
  • factor in generation of moist convection.
results2
Results
  • As expected, stronger zonal momentum figures prominently in the
  • occurrence of dryline-associated tornadic storms.
results3
Results
  • Generally, large low-mid tropospheric lapse rates favor LFC
  • attainment near initiation point, and severity of convective
  • development downstream.
results4
Results
  • Deeper-layer (T850-T500) and shallower-layer (T700-T500) lapse rates
  • do explain separate variance occasionally (Griesinger and Weiss, 1.5).
results5
Results

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

convection.

primary conclusions
Primary Conclusions
  • A continuum of dryline events exists – application of arbitrary specific humidity gradient thresholds removes weak dryline cases.
  • Background synoptic pattern influences dryline intensity.
    • The Rocky Mountain lee trough, specifically, is shown to be present for even the weakest of dryline events.
    • More confluent drylines tend to be more intense, though significant outliers exist.
  • Synoptic pattern and dryline characteristics influence initiation and severity of convection (continuing investigation).
    • Dryline intensity is a significant forcing factor for severity of subsequent convection.
    • Low to mid-tropospheric lapse rates near dryline are significant for initiation of deep moist convection; same lapse rates east of the dryline significant for severity of convection downstream.
    • 850-500 mb and 700-500 mb lapse rate can occasionally explain separate variance (where coefficients are opposite in sign).
types of potential testbed projects1
Types of Potential Testbed Projects
  • Composite sea-breeze events: events that move onshore vs. quasistationary events
  • Composite good/bad air-quality episodes
  • Strong versus weak inversions
  • Long-lived inversions or low-visibility cases
  • Can statistical prediction equations be developed given high-resolution observations (e.g., experience at the 2002 Winter Olympic Games suggests you don’t need a lot of data)?
links
Links
  • http://www.cdc.noaa.gov/Composites/Day
  • http://www.cdc.noaa.gov/Composites/Hour
  • http://www.cdc.noaa.gov/Composites/NSSL/Day
types of potential testbed projects2
Types of Potential Testbed Projects
  • What are characteristic errors associated with certain stations (stable layers near surface, precipitation)?
  • What are the NWP errors associated with a given case?
  • Instrument cross-comparison (particularly for remote-sensing data)
  • Can the “shelter effect” be quantified?
  • What is the effect of the mast on temperatures at the same level?
  • How good is the WXT for hail or drop-size distributions?
  • Automatic detection of weather phenomena
  • Advancing QC methods
societal economic and business impacts

Societal, Economic, and Business Impacts

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

No adverse

weather

Adverse

weather

Cost–Loss Ratio:

p(event) >=

(b–d)/[(b–d)+(c–a)]

then protect

Do Not Protect

Protect

types of potential testbed projects3
Types of Potential Testbed Projects
  • How are business decisions by a certain company or a business sector affected (or could be affected) by access to Testbed data?
    • Construction: what kind of information do they need and with what specificity?
    • Calculate the cost–loss ratio for a specific business interest, for Testbed data and traditional data.
  • What is the value of high-resolution temperature/wind data for specific users (e.g., temperatures for electric companies at substations, as opposed to airports)?
  • A business prospectus for a specific company using Testbed data as an example.
  • Health and weather/climate studies (hospital and mortality statistics), weather event leads to more hospital visits in some part of Helsinki?