An Overview of the Proposed International H2O Project

(IHOP_2001)

David Parsons

National Center for Atmospheric Research

Atmospheric Technology Division

Overall Hypothesis

Will an improved characterization of the water vapor field will lead to a corresponding improvement in our ability to predict convective rainfall?

Outline

1. Why concentrate on improving quantitative precipitation forecasts (QPFs) for warm season rainfall over the U.S. continent?

2. Why try to improve QPFs through improving estimates of water vapor?

3. An overview of IHOP.

Monthly Threat Scores(adapted from Uccellini et al. 1994)

Flash Flood Frequency (adapted from Maddox et al. 1979 for 151

floods in a 5 year period)

Annual Threat Scores 1962-1995(adapted from several sources)

Manual-based Annual Threat Scores (>1" rainfall in 24 h)

0

0.05

0.1

0.15

0.2

0.25

0.3

62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95

Year

Th

rea

t s

co

re

Steady Improvement in Wind Forecasts

Practical Relevance• “Floods, especially flash floods, kill more people each year than tornadoes, hurricanes,

lightning and wind storms” (USA Today 1999)• “Flash floods have become the convective related event annually producing the most fatalities”

(Doswell et al. 1999)• Significant property losses due to flash floods (>$2 billion per year 1983-1992, U.S. Army

Corps of Engineer 1993). Illinois damage alone from 1993 flood was $1.4 billion. Annual damage rising rapidly partly due to increased development. Highest percentage of federal aid events

• “A commonly observed problem with floods is that early damage estimates are typically much too low” (Changnon 1996). Additional human impacts include severe emotional stress, large increase in auto accidents, transportation delays, evacuations, personal and interagency conflicts, erosion, drinking water problems, clean-ups, etc.

• Warnings are difficult: often night time events, public perception of precipitation events, dependent on hydrology and meteorology, and some argue the “system” is not as well developed as for tornadoes

• It has been argued that improvements in QPF for non-flood situations will have significant economic and social benefit.

Heavy rainfall is a National Problem.• Examples in the recent (1997-current) literature.

– Fort Collins, Colorado (July, 1997, 5 deaths, losses of $200 millions)

– Southern Nevada (August 1997, $350 million damage to uninsured property alone)

– Chicago metropolitan area (July 1996, losses of $645 million, 35,000 homes damaged, $67 million in farm income lost, 6 deaths)

– Erie, Pennsylvania (Sept. 1996), numerous Hawaiian floods (see Kodama and Barnes

1997), Tucson, Arizona events (see Holle and Bennett 1997)

– Madison County, Virginia (June, 1995, 3 deaths, losses of $200 million plus cleanup, 400 roads closed, 80 bridges and 2000 homes damaged or destroyed).

– Extreme rainfall embedded within the 1993 Great Midwest Flood (e.g., S of Kansas City, Missouri and near Des Moines, Iowa)

– Gallus (1999) (western Iowa, July 1996 and south Texas, May 1997) – Several extremely damaging situations in the 1970s (Rapid City, S.D., Big Thompson,

CO, Johnstown, PA with 70 deaths, New Orleans, Palo Duro, TX, and Rochester, Minn.)

Summary: Why an experiment aimed at improving warm season QPF over the

continental U.S. ?

• The lowest QPF skill is during the warm season, while the highest risk for flash floods occurs during the warm season with weak synoptic forcing.

• Relatively poor skill in forecasting heavy rainfall with relatively slow progress.

• Public safety and numerous arguments on practical relevance.

• Improving QPF for heavy convective rainfall is a national, not a regional, problem.

Why Try to Improve QPFs Through Better Characterization of Water Vapor?

• Recommendation of the First Prospectus Development team of the U.S. Weather Research Program to NOAA and the NSF (Emanuel et al. 1996)– “Improvement in numerical forecasting, and especially

quantitative precipitation forecasting, is severely impeded by poorly resolved and inaccurate measurements of atmospheric water vapor. High priority must be given to new water vapor measurement systems and to research that seeks to delineate the water vapor observations necessary to address specific forecast problems.”

Why Try to Improve QPFs Through Better Characterization of Water Vapor? • Recommendation of the 2nd Prospectus Development team of the U.S.

Weather Research Program on research opportunities from emerging atmospheric observing and modeling capabilities (Dabberdt and Schlatter 1996)

– “Progress in precipitation forecasting is impeded by poorly resolved and inaccurate measurements of atmospheric water vapor.”

– “There is a need to conduct a comprehensive pilot field measurement and mesoscale modeling study to test the efficacy of possible composite operational water vapor observing systems and adaptive sampling strategies, including those pertaining to satellite derived moisture analysis for regional-scale applications.”

– “Modeling, measurement and analysis efforts <for water vapor> must focus on a broad spectrum of existing and emerging technologies.”

Why Try to Improve QPFs Through Better Characterization of Water Vapor?

• To predict the initiation of convection in cloud models and in nature, one needs a very accurate characterization of water vapor profiles within and just above the boundary layer (e.g., Crook 1996).

• It has long been known that the magnitude of the convective available potential energy (CAPE) and its ratio with the vertical wind shear help determine convective intensity and structure (e.g. Weisman and Klemp 1986). Water vapor in the boundary layer strongly impacts CAPE.

• Observations indicate significant mesoscale (Shapiro 1982) and convective scale (Weckwerth et al. 1996) variability in water vapor fields. This variability is often linked to the initiation of convection.

Mesoscale Variability in Water Vapor

A nocturnal dryline passage, followed by enhancedlifting as a weak cold front approached from thewest. Severe convection later developed. (Parsonsand Miller 2000).

Vertically Integrated Water Vapor

Why Try to Improve QPFs Through Better Characterization of Water Vapor?

• Water vapor is poorly measured– It is generally thought to be under-sampled (panel discussion of

1999 USWRP science team meeting) relative to the spatial and temporal gradients.

– Significant biases exist (3-6%) in radiosonde data. According to Vaisala and NCAR, these errors are due to sensor contamination after manufacture. These errors can impact our ability to detect CAPE and convective inhibition (Guichard et al. 1999)

– Remote sensing of water vapor in the lower troposphere suffers from significant performance limitations (i.e., there is no operational equivalent to the wind profiler). See Dabberdt and Hardesty (1990).

Summary: Why Water Vapor?• See work of the USWRP Prospectus Development Teams.

• Water vapor is poorly measured and under sampled.• Strong dependence of convective initiation and storms

structure/intensity on accurate estimates of water vapor.• Recent mesoscale data assimilation studies seem to indicate that a

direct measurement of the vertical profile of water vapor may be necessary (e.g., Guo et al. 1999). This finding is different from temperature and winds where the temperature predictions are improved assimilating wind profiler measurements. There is no general balance constraint for humidity.

• We acknowledge the potential improvement in QPF from other avenues (data assimilation and general model improvements).

IHOP_2001• WHEN: June/July or May/June 2001

• WHO: Investigators from the U.S., France, Germany and Canada

• WHERE: Southern Great Plains of the U.S. (Oklahoma/Kansas) taking advantage of the wind profiler network, ARM facilities, CASES domain, SGP domain, GPS network, Oklahoma mesonet, and U of Wisconsin ARM passive sounding systems

Water Vapor Techniques • Ground-based DIAL • Ground-based Raman lidar• Airborne DIAL • Radar refractivity measurements (scanning)• Wind profiler reflectivity alone and as a combined sensor approach• GPS ground-based for integrated water vapor• GPS tomography• Advanced in-situ aircraft measurement (DIAL)• Rawinsonde• Ground-based passive (radiometers and high-resolution interferometer sounder)• Satellite• In-situ sensors on commercial aircraft

Proposed Instrumentation• NCAR Electra aircraft (Primarily NSF deployment pool and support from French)

– French water vapor DIAL lidar– ELDORA Doppler radar– U of Wyoming cloud radar– In-situ sensors

• Surface refractivity measurements from a NEXRAD Doppler radar and/or NCAR’s S-POL (Internal NCAR funds or NSF deployment pool)

• Radiosonde launches (4 sites at 8 times per day by operating during an ARM IOP)• DLR Falcon with their water vapor DIAL system (Need partial U.S. support perhaps

through NSF grants program)• Ground-based water vapor lidars (ARM system, U of M at Baltimore system, CU-

NCAR/NOAA system) Need USWRP or NSF grants funding for deployment• Enhanced GPS ground-based systems (attempt at tomography)-ARM funded

Observations from a Ground-based Raman Lidar

Measurements through a cold front (courtesy of B. B. Demoz, U of Maryland at Baltimore County).

Estimates of Column Integrated Water Vapor from Ground-based GPS

Water Vapor from Radar Refractivity (courtesy of F. Fabry

at McGill)

Interested Participants• NOAA/NSSL (prediction of severe weather, instrumentation,

convective initiation)– C. Ziegler, D. Jorgensen, D. Blanchard

• NOAA/ETL (Combined sensor approach, lidar)– M. Hardesty, B.Stankov

• NOAA/FSL (GPS, data assimilation, numerical modeling)– K. Holus, N. Radziwill, J.-W. Bao, T. Schlatter, S. Gutman

• NOAA/OGP (Humidity measurements from commercial aircraft)– R. Fleming

• NOAA/CDC (water vapor and convection)– B. Mapes

Interested Participants• NCAR (various instrumentation, data assimilation, numerical modeling,

nowcasting, convective initiation and PBL studies)

– D. Parsons, T. Weckwerth, J. Wilson, L.J. Miller, A. Crook, M. Weisman, D. Lenschow, W. Brown, S. Cohn, W.-C. Lee, A. Heymsfield, L. Miloshevich, V. Wulfmeyer, J. Keeler

• UCAR (GPS)

– C. Rocken, J. Brown

• University (boundary layer processes, convective initiation, instrumentation, mesoscale processes)

– R. Wakimoto (UCLA), K. Davis (U Minn), B. Demoz (UMBC), S. Koch (NCSU), J. Machol (CU), M. Jensen (CU/CIRES), D. Randel (CSU/CIRA), D. Weber (CAPS/OU), A. Pazmany (UMASS)

Interested Participants• International (airborne lidar, radar refractivity)

– G. Ehret (DLR), F. Fabry (McGill), J. Pelon (France), C. Flamant (France)

• NASA (airborne lidar)

– E. Browell and LASE group

• DOE (ARM representation and water vapor measurement techniques)

– D. Turner (PNL)

• Private industry

– F. Solheim (Radiometrics)

Scientific Goals

1. Evaluate potential measurement and assimilation strategies for water vapor.

2. Explore the impact of improved water vapor measurements on QPF for nowcasting, and for cloud and mesoscale models.

3. Test performance of operational and research models for water vapor and water vapor processes.

4. Improved understanding of the role of 3-D variations in water vapor and how these variations impact the initiation and maintenance of convection

5. Better establish water vapor accuracy, precision for different measurement techniques.

6. Obtain data on operating characteristics for future instrument design.

7. Improved understanding of how sub-grid scale variations in water vapor and water vapor processes impact coarse grid models.

8. Investigate the link between boundary layer processes, water vapor and convection.

9. Possible “piggy back” of CO2 budget studies.