earth science vision 2010 – 2025: extreme weather scott braun (co-lead, nasa/gsfc) steve goodman...

Earth Science Vision 2010 – 2025: Extreme Weather

Earth Science Vision 2010 – 2025: Extreme Weather

Scott Braun (Co-lead, NASA/GSFC)Steve Goodman (Co-lead, NASA/MSFC)

Team membersRichard Anthes (UCAR) Craig Bishop (NRL) Jim Dodge (NASA) Kerry Emanual (MIT) Peter Hildebrand (NASA) Dan Keyser (SUNY)Chris Kummerow (CSU) Tim Miller (NASA/MSFC)Ralph Petersen (NOAA) Joseph Schaefer (NOAA)Chris Velden (UWisc)

Scott Braun (Co-lead, NASA/GSFC)Steve Goodman (Co-lead, NASA/MSFC)

Team membersRichard Anthes (UCAR) Craig Bishop (NRL) Jim Dodge (NASA) Kerry Emanual (MIT) Peter Hildebrand (NASA) Dan Keyser (SUNY)Chris Kummerow (CSU) Tim Miller (NASA/MSFC)Ralph Petersen (NOAA) Joseph Schaefer (NOAA)Chris Velden (UWisc)

Feb 4, 2002 ES Vision Plenary 2

ESV: Extreme WeatherESV: Extreme Weather

Science Questions: What are the intrinsic predictability limits of tropical cyclones in terms of their formation, movement, and intensity and what physical factors determine these limits? What are the observational and model deficiencies that limit the skill and accuracy of tropical cyclone predictions?


BackgroundBackground

• Large-scale predictability studies suggest error growth governed by most rapidly growing mode (e.g. thunderstorms) —hence, in theory, mesoscale predictability far more limited than large scale

• However, predictability on mesoscale exists because many mesoscale systems are controlled by

– Mesoscale inhomogeneities at surface (terrain, albedo, roughness, moisture availability)

– Linear and nonlinear internal modifications of the large-scale flow that lead to smaller-scale circulations

• Effects of large-scale deformation fields

• Effects of instabilities (barotropic, baroclinic, convective)

• Predictability more difficult for tropical cyclones (for intensity/precipitation) because knowledge of vortex structure is required



Schematic diagram illustrating relative con-tributions with increasing time of observa-tions and models to the total predictability (Anthes 1984)

• Total predictability depends on the

• quality of initial observations and data assimilation system

• quality of numerical forecast model

• Need to quantify limits in figure for formation, track, intensity, and precipitation prediction

• Observations are a major limitation (more like 10-20% than 60% in figure)

• Although observations and predictability at large scales are important, knowledge of vortex structure is critical



• Preliminary data for track predictability

– Abbey, Leslie, and Holland (1999)

– Compares 3 estimates of intrinsic limits with current practical skill in track forecasts

0

50

100

150

200

250

300

350

0 12 24 36 48 60 72

Time (h)

Intrinsic 1intrinsic 2Intrinsic 3Practical

Estimates of theoretical limit

Current skill


• Marginal precipitation prediction skill beyond simple rule of thumb — precipitation ~ peak rainfall/storm motion

• Little, if any skill, for tropical cyclogenesis


• Relatively poor skill for intensity

– Large errors (`88, `92) due to forecast busts of strong storms

– Small errors (`87, `90) due to lack of strong storms

– Slow improvement since 1980 when these years are considered



• Reasons to be hopeful of potential improvements

– Maximum potential intensity linked to ocean state (SST, mixed layer structure), upper tropospheric temperature

– Bogus vortices in models provide some improvement, but still lack adequate information on asymmetries, vertical structure

– Scatterometer surface winds provide some early detection

– Future coupling of surface winds and Global Precipitation Measurement (GPM) rainfall may aid forecasts


AssumptionsAssumptions

• By 2010, environmental measurements of temperature, water vapor will be available in cloud-free areas

– GIFTS

– NPOESS

– COSMIC (also provides some in-cloud measurements)

• By 2010, 3-h moderate-resolution surface rainfall will be available (Global Precipitation Measurement)

• Adequate wind data (except at surface) still unavailable


Current capability or limitationsCurrent capability or limitations

• Knowledge:

– Little understanding of formation and intensification processes

– What scales and physical processes determine intrinsic predictability limits?

• Observations:

– Inadequate wind observations in and out of clouds

– Inadequate in-cloud thermodynamic, precipitation measurements

– Inadequate observations of ocean structure (vertical structure, under clouds)

• Models:

– Grid resolution too coarse to resolve clouds

– Inadequate microphysical, boundary layer, ocean models

– Improvements to mesoscale data assimilation needed, especially information on error statistics


Current capability or limitationsCurrent capability or limitations

• How do we address the limitations in

– Knowledge?

– Observations?

– Models?


Research Agenda: What new Research Agenda: What new knowledge is required?knowledge is required?

• Improved understanding of the processes that control hurricane formation and intensity change

– Predictability limits may depend on the scale of the dominant processes (large-scale environment, mesoscale processes, convective scale processes) and their interactions

– Need to know relative roles of external vs. internal processes

• Role of environmental stability

• Role of large-scale vs. vortex-scale circulations (shortwave troughs, vertical shear vs. vortex asymmetries)

• Role of convective processes

• Role of SST anomalies, ocean mixed layer structure

– Need improved understanding of air-sea transfer


Research Agenda: What observations Research Agenda: What observations are required?are required?

• Need measurements in clouds!

– Winds (highest priority)

– Temperature, water vapor

– Precipitation structure (liquid/ice water contents)

• Need to observe ocean structure

– Sea-surface temperature under clouds/precipitation

– Mixed layer structure

– Wave fields


Specific Measurement NeedsSpecific Measurement Needs

• Wind profiles in cloud-free and cloudy areas

– globally: ~50-100 km, 3h sampling

– locally: ~5-25 km, 1h sampling

• Soundings of temperature, water vapor in cloudy areas

– ~5-25 km, 1h sampling

• Precipitation structure

– Surface rainfall every hour

– 3-D structure every 3 h

• Ocean measurements

– SST beneath clouds and precip ~ hourly

– vertical structure of mixed layer ~ twice daily

– wave fields ~ hourly


Measurement Needs: Possible platformsMeasurement Needs: Possible platforms

• Ultra-long duration high-altitude balloons or unmanned aircraft

– Balloons need propulsion to stay over storm

– Solar powered

– Doppler radar, wind profiler

– Miniaturized (cm-scale) dropsondes, airborne expendable bathythermographs (AXBT’s)

• Constellation of wind profilers or lidars, GPS receivers, Doppler radars in TRMM-like orbits

• Geostationary microwave radiometers, wind lidars


Research Agenda: What model Research Agenda: What model improvements are required?improvements are required?

• Coupled ocean-atmosphere, high resolution models that include:

– Fully coupled ocean model with evolving ocean and wave structures

– Accurate surface interactions and fluxes, including sea spray

– Nesting of grids capable of resolving large eddy structure of storms

– Fully explicit bin microphysics

• Ensemble modeling systems

– Genetically improving multi-scale modeling systems

• Diverse models, grid scales ranging from ~1 m to 15 km

• Models validated against new observations

• Good models survive and improve, bad models either change or disappear

• Higher resolution models validate parameterizations of coarser models

• Improved data assimilation through better analysis error statistics


Research Agenda: What data assimilation Research Agenda: What data assimilation improvements are required?improvements are required?

• Rapid and accurate mesoscale data assimilation

• Nonlinear particle filter data assimilation techniques

– Fully 4 dimensional

– Equal to or better than 4DVAR or Kalman Filter

– Well suited to highly non-linear phenomena and also to observations that are non-linear functions of the state variables; i.e. satellite observations


Benefits to PredictionBenefits to Prediction

• Early detection of developing storms, improved forecasts of whether disturbances will develop into tropical cyclones

• Improved intensity forecasts

– Better ocean measurements will improve estimates of potential intensity changes

– In-cloud measurements will improve initialization of models with realistic vortices

– Improved environmental measurements will better define external influences

• Improved track forecasts

– Improved environmental measurements will better define steering currents

– New observations and model may close gap between practical and intrinsic limits


ApplicationsApplications

• Observation network and model improvements can also be applied to other systems

– Non-developing tropical storms or remnants of stronger storms that often produce heavy rainfall and severe weather

– Extratropical cyclones and fronts

– Orographic systems (winter storms, thunderstorms, wind storms)

• Improved understanding of tropical cyclogenesis and intensification can be applied to predictions of future climate to estimate climate change impacts on storm occurrence, frequency, and intensity


Benefits to SocietyBenefits to Society

• Improved forecasts of formation, track, and intensity

• Reduction in areas of overwarning

• Public safety

– reduction of loss of life

– reduction in economic costs of severe weather

– improved disaster preparedness

• Improved economic efficiency

– enable decisions regarding new observing systems, model development

– activities directed away from time / locations of adverse conditions

– efficient protective measures

earth science vision 2010 – 2025: extreme weather scott braun (co-lead, nasa/gsfc) steve goodman...

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