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Meteorological Impacts and Benefits of AMDAR Data. Lee Cronce Ralph Petersen Cooperative Institute for Meteorological Satellite Studies (CIMSS) Space Science and Engineering Center (SSEC) University of Wisconsin - Madison. AMDAR Regional Science and Technology Workshop
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Meteorological Impacts and Benefits of AMDAR Data Lee Cronce Ralph Petersen Cooperative Institute for Meteorological Satellite Studies (CIMSS) Space Science and Engineering Center (SSEC) University of Wisconsin - Madison AMDAR Regional Science and Technology Workshop Mexico City, Mexico 9 November 2011
Atmospheric Data Realities • Vertical variation of temperature, moisture and wind within the atmosphere is what drives Atmospheric Science • In situ profiles of these data (i.e., radiosondes) remain the backbone of any NWP analysis system • The details within these profiles are especially important for recognizing and predicting hazardous weather events
Atmospheric Data Realities • Vertical variation of temperature, moisture and wind within the atmosphere is what drives Atmospheric Science • In situ profiles of these data (i.e., radiosondes) remain the backbone of any NWP analysis system • The details within these profiles are especially important for recognizing and predicting hazardous weather events • The locations of radiosonde sites are sparse, and the number of radiosonde reports is decreasing worldwide
Atmospheric Data Realities • Vertical variation of temperature, moisture and wind within the atmosphere is what drives Atmospheric Science • In situ profiles of these data (i.e., radiosondes) remain the backbone of any NWP analysis system • The details within these profiles are especially important for recognizing and predicting hazardous weather events • The locations of radiosonde sites are sparse, and the number of radiosonde reports is decreasing worldwide • Satellites provide global coverage; however, not at detail necessary (especially near the surface) • AMDAR fills this void!
AMDAR in a Nutshell • Temperature and Wind Observations from Commercial Aircraft • High quality, high resolution data • Available at: • Flight Level • In Ascent / Descent • Instruments already on aircraft • Economical (~100 times less expensive than radiosondes) • Asynoptic(not only available at 00 and 12UTC, not a problem for NWP) • Looks, feels, tastes like radiosonde data • Retrieved through ACARS and MDCRS
Usage of AMDAR Data WHO WOULD BENEFIT FROM THIS DATA SOURCE?
How does NCEP use AMDAR data?In all of its atmospheric models • NCEP runs a suite of Atmospheric and Oceanic models to meet a variety of user needs. AMDAR data are used in: • Climate (Coupling Atmosphere and Ocean) • Global (Medium Range Forecasts) • 4/day - Deterministic and Probabilistic • Mesoscale(Higher-Resolution Weather Forecasts) • 4/day - Deterministic and Probabilistic • Rapid Update Cycle (RUC) [soon to be Rapid Refresh Model] • Hourly - Aviation and Hazardous Weather • Mexico included in coverage, so immediate use available
How does NCEP use AMDAR data?In all of its atmospheric models • Vertical profiles of wind, temperature and humidity are the foundation of every NWP system • NCEP has been using AMDAR data in its NWP models for over 10 years • Over300,000 reports arrive daily • Data delivered in real-time 24 hours daily • Most contain wind and temperature only • Increasing numbers include humidity • The data arrive in BUFR format • The program is a cooperative venture between data providers and users • Everyone benefits from the results
How does NCEP use AMDAR data?In all of its atmospheric models “Rule of Thumb” • In Numerical Weather Prediction (NWP), one bad observation does more damage than the benefit that comes from 100 good observations! • AMDAR data are extremely accurate and reliable, but • Good Quality Control of all observations is essential • Requires multiple observations
A major advantage of AMDAR data – multiple observations corroborate each otherWeekly Data counts by Cycle
Data Volume/Coverage byLayers Six hours of data Note locations Of Ascent/descent Reports ←← 300-100mb 700-300mb 1000-700mb
Determining Forecast Improvement from increased AMDAR volume – Use Wind forecasts as a measure of impact General Observation • During weekday, when more AMDAR reports are available, short range forecasts are consistently better • 0000-1200 UTC (overnight) AMDAR volume average • Tu-Sa >70,000 reports • Su-Mo only ~25,000 reports • Difference is primarily due to lack of parcel delivery flights
Quantifying these Observations using theRapid Update Cycle - RUCRUC is designed to produce hourly analyses andupdates to very short range forecasts (0-12 hrs) • Real-time 1-hourly analysis/forecast cycle • Analyses intended to fit data very closely • Forecasts only from 3 to 12 hours into future • In general, 3 hr RUC wind forecasts are more accurate than 12 hr forecasts • Examination of verification against • Radiosonde observations
Weekend minus Weekday 3 hr Wind Forecast Errors for Jan-Oct • RUC Wind forecasts • Verification against raob data Off-time data on weekends produces less impact, especially after reduced overnight package carriers reports 0.35 m/s / ~5.0 m/s =7% better forecasts during weekdays due to more AMDAR reports at 200 hPa
Hourly AMDAR VolumeReceived at FSL (ESRL)2-15 Sept 01(starting 00z 2 Sept) 2-8 Sept 01 Su Mo Tu We Th Fr Sa 9-16 Sept 01 Notable reductions of aircraft data available to RUC at FSL on weekends and immediately after Sept. 11, 2001 Su Mo Tu We Th Fr Sa
Improvement in 3 hr over 12 hr wind forecasts during September 2001 Forecasts from operational RUC run at NCEP • 11-13 September 2011 • No AMDAR data • 20% loss of 3hr RUC wind forecast skill at 250mb • 3 hr fcst skill ≅ 12hr skill • No skill added by other off-time reports!!! Period of data outage 11-13 Sept 2001 RUC 250mb wind forecasts verified against raob data
Impact of AMDAR Ascent/Descent Data in Rapid Update Cycle (RUC) forecasts EMC OSE by Ralph Petersen, Geoff Manikin and Dennis Keyser • Test performed using operational 20 km RUC • Ran data assimilation / forecast system for 3 weeks in June 2002 using two configurations: • Including all data • Eliminated aircraft data below 350 hPa • Kept High-level En-route Data • Ignored Ascent /Descent Data • Compared analyses and all forecasts (3, 6, 9, 12 Hr) against radiosonde at 00 and 12 UTC over CONUS • Results expressed in improvement due to Ascent/Descent Data
Impact of AMDAR Ascent/Descent Data in Rapid Update Cycle (RUC) forecasts • Question 1 • What was the effect of the addition of ascent/descent data on the data assimilation system and resulting 00 and 12 UTC analyses?
Normalize error: compares forecast differences with overall forecast error • Significant improvement by including Ascent / Descent data • Positive effects at all levels • Greatest effect at 30,000’ and below • Positive impact on Winds, Temp and RH.
Impact of AMDAR Ascent/Descent Data in Rapid Update Cycle (RUC) forecasts • Question 2 • What was the effect of these analysis differences on the 12 hr forecasts?
Tropospheric Improvements up to twice those in changing RUC from 40 to 20 km • Significant improvement by including Ascent / Descent data • Positive effects at all levels on Winds, Temp and RH • Above 25,000’, impact comparable to analysis differences • Below 25,000’, impact still large - but slightly smaller than in analysis
Impact of AMDAR Ascent/Descent data in Rapid Update Cycle (RUC) forecasts • The fundamental purpose of the RUC is to use ‘off-time’ data to make repeated corrections to traditional ‘on-time’ model guidance • Question 3 • How did the continued data assimilation affect model performance?
Tropospheric Improvements are 2-3 times greater than those in changing RUC from 40 to 20 km • After 9 hrs of continued use of ascent/descent data, • tropospheric forecasts have improved by yet another 1-2%
Impact of AMDAR Ascent/Descent data in updating operational RUC forecasts Descent 10–20% improvement at all levels from forecast updates 12 Hr Forecast Error – Red 3 Hr Forecast Error – Blue Both forecasts valid at same times
Impact of AMDAR Ascent/Descent Data in Rapid Update Cycle (RUC) forecasts • The fundamental purpose of the RUC is to use ‘off-time’ data to make repeated corrections to traditional ‘on-time’ model guidance • Overall question now becomes: • How much of the impact was the result of including ‘off-time’ ascent / descent data?
Difference between 12 hr operational RUC forecast and a later 3 hr forecast (valid at the same time but using additional asynopticreports) from systems with & without ascent/descents Descent Lack of ascent/descent data in assim./fcsts eliminates virtuallyall tropospheric benefits of off-time updates and degrades upper-levels Assimilation/forecasts with Ascent/Descent Data– Red Assimilation/forecasts without Ascent/Descent Data– Blue
Local Applications • Severe Weather • Capping Inversions • Convective Instability • Wind Shear • Precipitation and Type • Timing, Location, Intensity • Fog Onset/Dissipation • Trapping Inversion Development/Decay • Calm Winds • Air Quality/Fire Weather • Wind, Stability, Mixing, Extended Coverage
Low-Level Wind Shear • Green Bay, Wisconsin, 29 October 2005 • LLWS was forecast to begin after 0600 UTC in the TAF • Aircraft soundings near 0120 UTC already showed LLWS • Based on this observation, the aviation forecaster was able to update the TAF and begin the LLWS more than 3 hours earlier than the prior forecast.
Low Ceilings, Visibilities and Fog • Detroit, Michigan, 4 February 2005 • Soundings near 2230 UTC showed light boundary layer winds, near-surface moisture, dryness above • Commonly favorable conditions for fog development • Based on the observations, the TAFs for 09 and 12 UTC were amended, reducing visibilities to ½ mile. • METARS showed that visibilities did decrease • KDTW 0532z 00000kt 2sm brclr • KDTW 0739z 17003kt 1 3/4sm brr04/ 1000v3500 • KDTW 0936z 17004kt 1/4sm fgr04/ 0500v0600 • KDTW 1154z 16004kt 1/4sm fgr04/ 2800v0600
Precipitation Type • Buffalo, New York, 15 December 2005 • Forecasters initially were calling for larger snow accumulations • AMDAR temperature profile shows a larger than expected • warm layer aloft • With the existence of this deep warm layer aloft, forecasters amended the forecast calling for smaller snow accumulations and increased chances for sleet and freezing rain
Convective Storms • Central Wisconsin, 6 July 2005 • Linear mesoscale convective system expected to persist into Wisconsin • Severe thunderstorm watch was issued at 1530 UTC for most of Central Wisconsin
Convective Storms • Aircraft soundings from watch area at watch issuance and later showed strong capping inversion unlikely to break • Forecasters lowered the chance for storms and the severe thunderstorm watch was cancelled
Fire Weather • Northern and Central Wisconsin, 15 June 2006 • Aircraft data showed extremely dry conditions coupled with the potential for high winds due to mixing • Very dry air could be seen on aircraft soundings earlier in the day when the Red Flag Warning was issued • Later soundings showed there was sufficient dry air in other parts of the forecast area to expand the warning • Temperature >75F, RH <25%, winds >25 mph
In Summary • AMDAR is a very important and necessary data set • Fills spatial and temporal voids apparent in radiosonde and satellite data sets • En-route data, but more so, ascent/descent data are vital to NWP skill • Not just a NWP benefit, but an important local forecast area data set