Overview of the International H20 Project - IHOP

Transcription

1 Atmospheric Measurements and Observations II EAS 535 Overview of the International H20 Project - IHOP Principal Investigators: Dave Parsons and Tammy Weckwerth All information in this lecture is attributed directly to the work of Parsons and Weckwerth and their colleagues as made available on the website:

2 Overview of IHOP Campaign Objectives Overview of IHOP instrumentation

3 IHOP_2002 Science Objectives and Components Improving Quantitative Precipitation Forecasts (QPF) Understanding Convection Initiation (CI) Effects of the Atmospheric Boundary Layer (ABL) on convection and exchange of moisture with the surface Finding the optimal mix of Water Vapor Instrumentation/Data

4 IHOP_2002 Science Objectives and Components Quantitative Precipitation Forecast (QPF) Research Component The QPF research component will primarily focus on the mesoscale distribution, evolution and spatial variation of water vapor in pre-convective and convective environments. Data assimilated from the IHOP_2002 network will be used to test the hypothesis that warm-season QPF skill can be significantly improved by better characterization of the four-dimensional water vapor field.

5 Quantitative Precipitation Forecasting How are NWP models currently performing? What is the current operational setting? What are the important desired improvements? What kind of measurements would aid in making these improvements? What additional forecasting aids are available? Did it work?

6 How are NWP models currently doing? Weckwerth et al, BAMS, 2004

7 Summer vs Winter Improving prediction in the warm season is important because hazardous weather (flash floods) often occurs in the summer. Primary priority of USWRP.

8 What is the current operational setting? Observation Network: Surface stations every 70 km P,T are representative of a large area q is not

9 Observation network 70 Radiosonde sites ~ every 500 km 2 times per day

10 Observation Network 160 Weather Surveillance Radar-1988 Doppler (WSR- 88D) sites

11 Earth and Atmospheric Sciences Observation Network 4km resolution GOES images

12 NWP How does it work? Define the domain and define a grid with an appropriate resolution. Get an initial guess of the model fields (ie from global model) Gather weather readings near each grid point (temperature, humidity, barometric pressure, wind speed and direction) at different altitudes Adjust your model so it fits the data and satisfies dynamical equations; Run the model by stepping it forward in time using the dynamical equations analyze and verify how accurately your model predicted the actual weather and revise it accordingly EASY inputting observations that are predicted by the model p, T, q, u Radiosonde, surface obs HARD inputting observations that are some function of model variables Radar reflectivity Satellite radiance

13 Typical data assimilated into NWP model local mesonetworks of surface observing systems Doppler radars, Selected satellite data, radiosondes wind and temperature (RASS) profilers (404 and boundary-layer 915 MHz), radiometric profilers, aircraft observations are assimilated at most every hour

14 Hypothesis: undersampling of water vapor The primary reason that convection (especially in the initiation stages) is not well predicted For example, convective rolls in the boundary layer can provide quite different profiles whether sampling updraft or downdraft. Parsons, 2002

15 What are the important desired improvements? Improved understanding and prediction of convective onset. Determine the degree of improvement in forecast skill that occurs through improved characterization of the water vapor field (0-6/12 h) in an environment where the dynamics are relatively well captured. Improved understanding and treatment of the relationship between atmospheric water vapor, soil moisture, surface fluxes and boundary layer processes. Improved assimilation of water vapor measurements for warm season events (including satellite data). Determine the future optimal mix of satellite, ground-based and surface water vapor measurement strategies for warm season rainfall.

16 What were the measurement strategies used to improve the performance? More water vapor measurements Increased density Airborne measurements During IHOP May 13 June 25, 2002 >200 Investigators and technical participants ~2500 additional soundings > 50 instrument platforms, 6 aircraft, 36 IOPs 268 h of airborne water vapor lidar measurements 76 h of airborne satellite evaluation measurements Dedicated GOES-11 data, every 5 minutes

17 What were the modeling strategies used to improve the performance? Surface fluxes => ties in with boundary layer observations Moisture initialization => for example hot start from derived cloud fields Higher resolution real time 4 km LAPS and MM5 model runs

18 LAPS II Three-Dimensional Cloud Analysis METAR Schultz, 2004 METAR METAR

19 Assimilated vertical motions into LAPS Cumulus vertical motions Schultz, 2004

20 LAPS II Dynamic Balance Adjustment FH ω c T ˆ > 0 q = q s FL Schultz, 2004

21 Did it work? Improvement over campaign duration Earth and Atmospheric Sciences

22 How does a specific instrument advance QPF? NCAR GPS Dropsonde Wang, 2003

23 Given this general concept of typical situations: Low Level Jets (maximum in wind speed profile at lower altitudes) often govern the transport and convergence of moisture which can lead to convection

24 How does a specific instrument advance QPF? Improved understanding and prediction of convective onset. Determine the degree of improvement in forecast skill that occurs through improved characterization of the water vapor field (0-6/12 h) in an environment where the dynamics are relatively well captured. Improved understanding and treatment of the relationship between atmospheric water vapor, soil moisture, surface fluxes and boundary layer processes. Improved assimilation of water vapor measurements for warm season events (including satellite data). Determine the future optimal mix of satellite, ground-based and surface water vapor measurement strategies for warm season rainfall.

25 MLLJ on June 9 ( UTC) Earth and Atmospheric Sciences 1. Box flight path (clockwise) 2. Clear sky in the domain 3. LLJ on the northern leg 4. Lear: 48 (took off from NW corner, ~50 km, two box flights) 5. Falcon: 21 (took off from SE corner, ~50 km) 6. Mapping moisture and intercomparison with DIAL, LASE, NAST Wang, 2003

26 MLLJ (East-West on the Northern Leg on June 9) Inversioncapped moist layer Earth and Atmospheric Sciences Two-layer moisture DIAL on Falcon Specific Humidity (g/kg) Wang, 2003

27 Discussion Example: Dropsondes Earth and Atmospheric Sciences Where could dropsondes help in the specific efforts for advancing QPF? What would you try to measure and why? Where would you measure and why? How often would you measure and why? What might possible limitations of the instrument be? Given some limitations, how would that affect the observation strategy?