hailstorm at 19 august over berlin and issues of verification of deep convection
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Hailstorm at 19 August over Berlinand Issues of Verification
of Deep Convection
Matthias Jaeneke, DWD
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Outline
Introduction
Why Case-Studies in Verification ?
The Berlin Hailstorm and Forecast-Verification Local Data, Synoptics, Short-Range DevelopmentTime-Series of Model Weather Verification
What does the Berlin Hailstorm Case teach us for Deep Convection Case Verification ?
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Introduction
• Why Case-Studies in Verification ?
Case-studies provide insight into strengths and weaknesses of forecasts of the single weather situation
Case-studies are able to stratify verification into typical classes of weather cases, for instance high impact situations
Case-studies are more adapted to problems and needs of the operational forecaster and are able to show up the real range of day to day variability of forecast quality
Case-studies are therefore integral part of all verification and represent all qualitative aspects of forecasts
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Direction of Wind
Maximum Gusts
Visibility
MSLP
Temperature 2mTemperature 5cm
Precipitation (1 min.)
5Total Precipitation Amount (mm) during the Hailstorm
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B
Geopot. 500T850
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B
Geopot.500KO-INDEX :Generalized verticalgradient of ThetaE
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B
Geopot. 500IR-SAT
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Surface Map
19-08-2000 12 UTC
Meteor.InstitutFree University Berlin
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B
NOAA-IR-DIAGT2m
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B
NOAA-IR-DIAGT2m
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B
NOAA-IR-DIAGT2m
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B
NOAA-IR-DIAGT2m
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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BBB
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
Radar-ReflectivityWarning-Markers
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-Weather1h Lightnings
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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B
LM: Grid-WeatherWeather-Observations
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What does the Berlin Hailstorm Case teach us
for Deep Convection Case Verification ?
• There are three questions to be discussed in connectionwith the Berlin Hailstorm case :
What is concerning verification the characteristics of deep convection and its NWP forecasts ?
Are standard measures of contingency table statisticsapplicable in single case verification ?
What was the quality of the NWP forecast for the Berlin Hailstorm Case and what do forecasters want to have ?
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What does the Berlin Hailstorm Case teach us for Deep Convection Case Verification ?
• What is concerning verification the characteristics of deep convection and its NWP forecast ?
Deep convection bears, though in nature deterministic, inits realization some stochastic characteristics
Deep convection forecasts are, like in the DWD-LM, expressedmostly as categorical grid-point „thunderstorm-activities“. Therefore they are also more probability than categorical type.
Also the Berlin Hailstorm shows : NWP forecasts provide„carpets“ of convection, in contrast to that real thunderstorms are structured in clusters and cells.
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What does the Berlin Hailstorm Case teach us for Deep Convection Case Verification ?
• Are standard measures of contingency table statistics applicable in single case verification ?
In situations of deep convection three different cases may occur:
(1) There are „yes“-forecasts and „yes“-observations(2) There are „yes“-forecasts but no „yes“-observations(3) There are „yes“-observations but no „yes“ forecasts
Case (1) may show overlapping of forecast and observations areaswith no problems for POD, FAR, BIAS and CSI. If no overlapping occurs (no hits) evaluation results in POD = 0, FAR = 1, CSI = 0and BIAS is determined by the ratio of both „yes“-areas.
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What does the Berlin Hailstorm Case teach us for Deep Convection Case Verification ?
• Are standard measures of contingency table statistics applicable in single case verification ?
For Case (2) with no „yes“-observation and therefore also no hits,POD and BIAS are not defined, FAR = 1 and CSI = 0
In case (3) with no „yes“ forecast and again no hits, POD =0,BIAS = 0 , CSI = 0 and FAR is not defined
In cases 1a, 2 and 3 the contingency table measures reveal bad forecast accuracy, partly quality assessment even is not possible.
Though this was partly true also in the Berlin hailstorm the forecaster recognized spatial and temporal „near-by“ cases to say : „The forecasts were not too bad, I got signals for severe convection.“
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What does the Berlin Hailstorm Case teach us for Deep Convection Case Verification ?
• What was altogether the NWP forecast quality in the Berlin hailstorm case? What do forecasters want to have ?
The LM forecasts of significant weather in the Berlin Hailstormcase gave reasonably good synoptic scale indication of what could happen in Central Europe at that day.
Despite systematic differences in daily run and mesoscale structure between forecast („carpet“) and observation (clusters and cells) the forecaster was able to extract an appropriate forecast signal.
The most valuable forecast signal was the mesoscale indication of hailstorm-pixels (intensity), despite of some errors in position and time. The forecaster wants this signal. The NWP forecast was useful for him.
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