Presentation on theme: "1 NEW OBSERVING SYSTEMS, NAOS, AND TARGETED OBSERVATIONS Presentation at the COMAP Symposium on Numerical Weather Prediction, NCAR, Boulder, CO 13-17 December."— Presentation transcript:
1 NEW OBSERVING SYSTEMS, NAOS, AND TARGETED OBSERVATIONS Presentation at the COMAP Symposium on Numerical Weather Prediction, NCAR, Boulder, CO 13-17 December 1999 by Tom Schlatter NOAA Forecast Systems Laboratory
2 OUTLINE How does this talk relate to NWP? What observations are only recently being assimilated into operational U.S. models? What you need to know about automated aircraft reports and profiler data with regard to NWP and nowcasting in the WFO The North American Atmospheric Observing System (NAOS) program - What problems does it tackle? Targeted observations
3 How does this talk relate to NWP? Observations feed prediction models. For short forecasts, accuracy of initial state is more of an issue than realism of model. Observations used to gauge accuracy of evolving forecast, to formulate the nowcast (30-90 minute extrapolation). ACARS and profiler observations, separately and together, have led to improvements in tropospheric predictions of temperature and wind. New products from these sources can also help you improve your nowcast. NAOS strives to put logic behind the proliferation of observing systems. What observing strategies are best for NWP? Targeted observations: put them where they make the most difference in downstream forecast accuracy.
4 What observations are only recently being assimilated into operational U.S. models? Note: Listed data sources are being assimilated into at least one of these three NCEP models: global spectral, Eta, and RUC-2. VAD winds from WSR-88D raw radiances from NOAA satellites precipitable water vapor estimates from GOES and NOAA satellites scatterometer data (to infer winds at sea surface) ACARS en route and ascent/descent data boundary-layer profilers of opportunity Radio Acoustic Sounding System high density cloud-drift winds from GOES
5 Coming Soon raw radiances from GOES satellite radial winds from WSR-88D water vapor drift winds from GOES precipitable water vapor estimates from GPS receivers on the ground This lecture focuses on those observation sources highlighted in red.
6 What’s new with ACARS? More reports More airlines participating in data collection Increasing percentage of ascent / descent reports Web page available to WFOs Water Vapor Sensing System
13 Web Address ACARS / MDCRS real-time data, including ascent / descent soundings http://acweb.fsl.noaa.gov/java/ Access is restricted. If you work in a WFO, you are entitled to reach this page. But first, get on the “customer” list by writing: firstname.lastname@example.org
14 Water Vapor Sensing System (WVSS) Status Report as of May 1999 WVSS refers to a water vapor sensor installed on commercial aircraft that delivers relative humidity values along with wind and temperature as part of the ACARS/MDCRS report.
15 Progress As of January 1999, 104 aircraft-weeks of WVSS reports (~190,000) had been collected. Two different aircraft obtained the measurements. With careful calibration, the WVSS delivers good humidity information under a wide variety of conditions, including in the high troposphere. Errors range from ~4% in mid-troposphere at low Mach numbers to ~17% in the high, cold troposphere at high Mach numbers. 5 UPS aircraft now equipped with WVSS NOAA owns 60 total systems; within ~12 months, about half should be installed on UPS and the other half on American jets.
16 Problems UPS has been very slow to add new sensors to its fleet. Other airlines have expressed interest but have not yet agreed to install the sensors on their aircraft. Some airlines prefer a single probe for temperature and humidity rather than two separate probes, as is the case now. This is prompting a redesign of the WVSS and requires new FAA approval--a lengthy process.
17 Three methods of measuring moisture by aircraft are viable today: Thin-film capacitor (polymer) Chilled mirror Near-infrared laser diode Fast response time is critical. Best bet: near-infrared laser diode
18 What’s new in atmospheric profiling? Potential of 6-min data Potential to infer vertical gradient of mixing ratio Loss of frequency allocation for NOAA Network Boundary-layer profiler data and RASS data available on the Web Processing GPS signals to obtain total precipitable water
21 Receiving Data Routinely Intermittent Data Availability And Possible Expansion Sites Boundary Layer Profiler Network December 1999
22 The Radio Acoustic Sounding System (RASS) Operates in conjunction with a wind profiling radar Sound waves emitted upward from the ground When the acoustic frequency of the sound waves is just right, the profiler can sense the velocity of the sound waves as a function of height. The speed of sound c is related to the virtual temperature T v through c = ( Rt v ) 1/2 where is the ratio of specific heat at constant pressure to that at constant volume for dry air, and R is the gas constant for dry air. T v = (c / 20.047) 2 when c is in m s -1 and T v is in degrees K.
26 Addition of Calibrated GOES-8 TPW Improves the Spatial Resolution of GPS-only IPW Data. 5 NOV 1997 1500 UTC NCAR_IPWPPT
27 Current Configuration of GPS Water Vapor Demonstration Network Dec 1999
28 GPS-IPW Demonstration Network in 3-5 Years Dec 1999
29 Web Address For comprehensive information about: NOAA Network Profilers and RASS Boundary-layer profilers Surface-based estimates of total precipitable water vapor from GPS Go to: http://www-dd.fsl.noaa.gov/profiler.html
30 Introduction to NAOS NAOS - North American Atmospheric Observing System Program to make recommendations on the configuration of the upper air observing systems over North America and adjacent water areas NAOS Council has representatives from 15 agencies in U.S., Canada, and Mexico to identify issues, set priorities, coordinate work of the program, and seek financial support NAOS Test and Evaluation Working Group –Assesses potential effects of proposed observing systems and configurations on the overall efficacy of forecasting services. –Assessments involve tests of hypotheses concerning the sensitivity of forecast accuracy to specific mixes of observing systems. –Assessments must also consider utility of data to field forecasters, who use them subjectively, and to the climate community.
31 Hypothesis 1 It will be possible to reduce the number of rawinsondes in the U.S. network without noticeably reducing forecast accuracy provided that the sites removed have substitute observing systems already in place. Test in two steps: Identify rawinsonde sites close to busy hub airports. At these sites withhold rawinsonde and all potential substitute observations for the periods covered by the sensitivity test. Compare forecasts generated from reduced data set with operational forecasts. Restore all substitute observations but continue to withhold rawinsondes. Compare these forecasts with operational forecasts.
32 Selection criteria for matching raob sites and hub airports 1) Average number of ascents or descents per day (fewer on weekends) 2) Distance from the airport to the raob site 3) Expected similarity in climate between the airport and raob site 4) Average number of points in aircraft "slant” sounding 5) Impact of deletions on overall uniformity of rawinsonde distribution 6) Don't touch GCOS sites.
33 Match-ups RaobAirport# Ascents and Descents / week Salem ORPortland~100 Oakland CASan Francisco>500 Desert Rock NVLas Vegas>100 Salt Lake City UTSalt Lake City50-80 Santa Teresa NMWhite Sandsprofiler Denver CODIA~500 Fort Worth TXDallas/Fort Worth~ 80 Topeka KSKansas City(MCI)40-70 Chanhaussen MNMinneapolis (MSP)~ 20 Buffalo NYToronto25-30 Peachtree City GAAtlanta~ 50 Slidell LANew Orleans~ 75 Miami FLMiami (MIA)~ 65 Upton NYNew York City (JFK)>150
34 Three tests conducted with each model: Global spectral model (MRF) Eta model Rapid Update Cycle (RUC-2) model Control run: included raobs and ACARS ascent / descent Experiment 1: raobs from 14 sites withheld Experiment 2: raobs and nearby ascent /descent data were withheld
35 Results from tests with the global spectral model Analyses: Differences between the control and the two experimental runs much less than difference between ECMWF and NECP analyses on a given day Forecasts: As many forecasts improved as degraded when raob data were withheld; effects were uniformly quite small. Slight signal of degradation when both sources withheld. Moisture fields affected initially by loss of raobs but effect quickly disappeared, in less than 12 h. Results from tests with Eta model: Similar
36 RUC analysis RUC 12-h forecast TEMPERATURE WIND Control minus Experiment statistics
37 RUC analysis RUC 12-h forecast HEIGHT RELATIVE HUMIDITY Control minus Experiment statistics
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