Satellite-based Convection Nowcasting and Aviation Turbulence Applications

Transcription

1 Satellite-based Convection Nowcasting and Aviation Turbulence Applications Kristopher Bedka Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison In collaboration with Wayne Feltz (CIMSS), Justin Sieglaff (CIMSS), and John Mecikalski (University of Alabama in Huntsville)

2 UW-CIMSS MSG SEVIRI Convection Research: Meeting Satellite Product Requirements for the GOES-R ABI Program The U.S. GOES-R Advanced Baseline Imager (ABI) program has the following convection and aviation requirements, for which objective algorithms need to be developed in upcoming years: 1)Convective initiation nowcast 2)Enhanced-V (i.e. Cold-V) and Overshooting Top detection 3)Lightning detection (GOES-R Lightning Mapper) 4)Turbulence (Clear Air and Convectively-induced) 5)Microburst wind speed potential 6)Atmospheric stability/thermodynamic retrievals MSG SEVIRI, MODIS, and cloud-resolving NWP simulations are used as proxy for future ABI data SEVIRI and ABI share many common spectral channels

3 Why Satellite-Based Convection Nowcasting? Satellite imagery provides the first indicators of vertical convective cloud growth Satellite-observed cloud growth can precede significant radar echoes by 60 mins, providing operational forecasters with valuable lead time in forecasting convective initiation (CI) CI=The first occurrence of a 35 dbz radar reflectivity LI=The first detection of lightning (either in-cloud or cloudto-ground) hours Operational NWP has difficulty in correctly forecasting CI timing and location High resolution, cloud-resolving (Δx < 10 km) NWP guidance provides one scenario of what COULD happen, whereas satellite imagery shows what IS happening CHALLENGE: Combine spatial, temporal, and multi-spectral satellite imaging capabilities in the development of convection nowcasting products

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5 Why Cloud Top Cooling Rates for CI Nowcasting? Rapid cumulus cloud growth, coupled with a temperature drop below 0 C in satellite IR imagery precede significant radar echoes (~35 dbz from 10 cm S- band) by 45 minutes (Roberts and Rutledge, WAF, 2003) Minimum 10.7 μm Cumulus BT Median 10.7 μm BT cooling rate

6 Why Cloud Top Cooling Rates for CI Nowcasting? Observations of 10.8 μm IR window BT trends over South Africa show similar results to those over the U.S. Satellite-observed cloud-top cooling precedes significant radar echoes by ~45-60 mins. Need similar studies using radar and satellite data over Europe! R. Matthee and E. deconing: FEB 19, 2008 Case (South African Weather Service) Cooling IR Window BT Convective Initation

7 Why Cloud Top Cooling Rates for CI Nowcasting? Observations of 10.8 μm IR window BT trends over South Africa show similar results to those over the U.S. Satellite-observed cloud-top cooling precedes significant radar echoes by ~45-60 mins. Need similar studies using radar and satellite data over Europe! R. Matthee and E. deconing: FEB 20, 2008 Case (South African Weather Service) Cooling IR Window BT Convective Initation

8 MSG CI Nowcasting Criteria: Case CI Interest Field Critical Value 10.8 µm T B < 0 K 10.8 µm T B Time Trend Timing of 10.8 µm T B drop below 0 C < -4 K/15 mins ΔT B /30 mins < ΔT B /15 mins Within prior 30 mins µm T B Difference < 0 K µm T B Difference* -3 to 0 K CAPE > 500 J/kg * Inoue (J. Meteor. Soc. Of Japan, 1987)

9 MSG CI Nowcasting Sensitivity: Case Channel Differencing Thresholds, No Stability Test (2 criteria)

10 MSG CI Nowcasting Sensitivity: Case IR Window Temperature (< 0 C) and Channel Differencing Thresholds, No Stability Test (3 criteria)

11 MSG CI Nowcasting Sensitivity: Case IR Window Temperature (< 0 C) and Channel Differencing Thresholds, With Stability Test (4 criteria)

12 MSG CI Nowcasting Sensitivity: Case All Criteria, Including IR Window Cooling Rates, With Stability Test (6 criteria) Use of cloud-top cooling rate information properly identifies newly developing cumulus clouds Accurate computation of cloud-top cooling rates is essential for objective nowcasting of convective storm initiation

13 MSG CI Nowcast: at 1100 UTC Nowcast: 1100 UTC Newly developing cumulus pixels in red HRV Reflectance: 1100 UTC 1100 UTC 1140 UTC

14 How to Compute Cloud Top Cooling: Bedka and Mecikalski (WAF, 2005) AMV-Based Method U=10 ms -1 u=u * cos( ) = 7.07 ms -1 pixel_x=(u*( t))/ x =~6 pixels v=u * sin( ) = 7.07 ms -1 pixel_y=(v*( t))/ y =~6 pixels ~1 km t-15 mins T b = - 40 C Current T b =20 C T b = - 50 C 10 ms -1 Simple Differencing AMV Differencing T b = - 70 C T b = - 10ºC T b = 60 C T b = - 10ºC

15 How to Compute Cloud Top Cooling: UW-CIMSS Box-Averaging Method Box size: 0.3 by 0.3 degrees A box is centered on every pixel in image; looping through every pixel; Running average results in significant overlap, which leads to good spatial coherence For each box: For both times, each box must be composed of at least 5% cumulus cloud (from UAH cumulus mask) to be included in cloud top cooling product 10.7 µm brightness temperature of every cumulus flagged pixel is included in the box mean Logic developed to handle complex box composition

16 COPS Experiment Region UAH Convective Cloud Mask is a daytime only product, as Visible channel texture and albedo is used in addition to IR T B and channel differences Free State Lesotho

17 Event Total Cloud-Top Cooling Rate over South Africa Event total cooling visualization allows us to evaluate the performance of cooling rate algorithms over the full duration of an event Box-averaged cloud top cooling provides better spatial coherence in cooling field than AMV method Lightning strike information is a better objective validation dataset here due to: 1) noise and clutter in the radar reflectivity field and 2) complex topography that can block radar detection of storms Data courtesy of the South African Weather Service (D. Minne and E. deconing)

18 Event Total Cloud-Top Cooling Rate over South Africa: ANIMATION Data courtesy of the South African Weather Service (D. Minne and E. deconing) Click here to download animation

19 Temporal Resolution Effects on Cloud- Top Cooling Rates Use of 5-minute rapid scan SEVIRI data better highlights growth of individual convective cells and results in fewer false alarms FALSE ALARMS

20 Temporal Resolution Effects on Cloud- Top Cooling Rates: ANIMATION Click here to download animation

21 Validation of Convective Initiation Using Cloud-to-Ground Lightning Objectively Recognized Lightning Initiation (LI) Locations Objective validation of convection nowcast products is difficult for several reasons 1)Satellite parallax produces error in representing the true location of a given cumulus cloud 2)Cloud motions as observed by satellite can be different than radar echoes 3)Difficulty in matching pre-ci satellite indicators to radar and lightning observations mins later Methods have been developed to account for these issues, which allows us to: 4)Understand satellite IR BT characteristics at the time of LI 5)Determine the accuracy of LI nowcasts with respect to cloud-to-ground lightning

22 Lightning Initiation and IR Window BT Relationships Analysis of Lightning Initiation Observations over South Africa from Feburary 19-21, 2008 Show the Following: Minimum IR Window BT At Lightning Initiation Time The first cloud-to-ground lightning strikes are most frequently observed in clouds with 10.8 μm BTs between K

23 Lightning Initiation and IR Window BT Relationships Analysis of Lightning Initiation Observations over South Africa from Feburary 19-21, 2008 Show the Following: Cumulus clouds are rapidly developing (15-30 K/30 mins) in advance of lightning initiation Assuming pre-li cloud has 10.8 μm T B of 275 K and 10 K/15 min growth rate, the maximum lead-time for nowcasting lightning initiation using satellite cloud-top cooling is ~45-75 mins

24 Lightning Initiation and IR Window BT Relationships Analysis of Lightning Initiation Observations over South Africa from Feburary 19-21, 2008 Show the Following: The box-averaged cloud-top cooling method is identifying first cumulus growth signals mins in advance of lightning initiation (LI) Lightning Initiation Validation Results* Probability of LI Detection (POD): 40% LI Nowcast False Alarm Ratio (FAR): 22% * Box-averaged method uses 10.8 μm IR window BT, 30 min cooling rates, and recent IR window BT drop below 0 C to identify LI nowcast pixels * AMV-based method POD: 27%, FAR: 35%

25 The Future: Day/Night Nowcasting Using Cloud Microphysics Advantages from using IR-only microphysical retrievals for CI/LI nowcasting - Day/Night nowcast capability - Explicit monitoring of phase changes, rather than inferences from reflectance, BT, and channel differences. - More information available to screen out potential false alarms - Product is fast to produce, 7 mins to process cloud phase product and cooling rate/nowcast over SEVIRI full disk

26 Day/Night Nowcasting Using Cloud Microphysics ANIMATION Click here to download animation

27 Convective Initiation Impacts on Commercial Aviation According to the U.S. Federal Aviation Administration (FAA), 76% of air traffic delays are a result of weather in the U.S. - Thunderstorms are responsible for 24% of weather delays In-flight turbulence is the leading cause of injuries to airline passengers and flight attendants approximately 58 people are seriously injured and >1000 with minor injuries as a result of turbulence each year in the U.S. (Page, Aviation Today, 2008) Turbulence is often observed by commercial aircraft as they fly within and above newly developing convective storms A new objective turbulence observation called Eddy Dissipation Rate (EDR) is being collected by United Airlines Boeing 757 aircraft - This EDR dataset, collected every 1 minute during flight, provides objectivity and improved spatial and temporal accuracy over traditional pilot turbulence reports (PIREPS) - Also included are non-turbulent (null) observations which are equally valuable We can plot this EDR data upon satellite imagery to learn about cloud-top signatures and time evolution of turbulent convective storms

28 Turbulence From Convective Initiation Turbulence Observed by Boeing Min After Image Time FL ft: T=205 K IR Satellite Cloud Temp=233 K Red: Severe Turbulence Green: Moderate Turbulence Blue: Light Turbulence Grey: No Turbulence A: Flight Above Cloud Top, Aircraft Temp Colder Than IR Window Temp B: Flight Below Cloud Top, Aircraft Temp Warmer Than IR Window Temp C: Clear Sky, Aircraft Temp Significantly Colder Than IR Window Temp I: Flight Within Cloud Top, Aircraft Temp Near IR Window Temp

29 Turbulence From Convective Initiation Turbulence Observed by Boeing Min After Image Time FL ft: T=205 K IR Satellite Cloud Temp=233 K Red: Severe Turbulence Green: Moderate Turbulence Blue: Light Turbulence Grey: No Turbulence A: Flight Above Cloud Top, Aircraft Temp Colder Than IR Window Temp B: Flight Below Cloud Top, Aircraft Temp Warmer Than IR Window Temp C: Clear Sky, Aircraft Temp Significantly Colder Than IR Window Temp I: Flight Within Cloud Top, Aircraft Temp Near IR Window Temp

30 Turbulence From Convective Initiation Turbulence Observed by Boeing Min After Image Time FL ft: T=205 K IR Satellite Cloud Temp=233 K Red: Severe Turbulence Green: Moderate Turbulence Blue: Light Turbulence Grey: No Turbulence A: Flight Above Cloud Top, Aircraft Temp Colder Than IR Window Temp B: Flight Below Cloud Top, Aircraft Temp Warmer Than IR Window Temp C: Clear Sky, Aircraft Temp Significantly Colder Than IR Window Temp I: Flight Within Cloud Top, Aircraft Temp Near IR Window Temp

31 Turbulence From Convective Initiation Turbulence Observed by Boeing Min After Image Time FL ft: T=205 K IR Satellite Cloud Temp=233 K Red: Severe Turbulence Green: Moderate Turbulence Blue: Light Turbulence Grey: No Turbulence A: Flight Above Cloud Top, Aircraft Temp Colder Than IR Window Temp B: Flight Below Cloud Top, Aircraft Temp Warmer Than IR Window Temp C: Clear Sky, Aircraft Temp Significantly Colder Than IR Window Temp I: Flight Within Cloud Top, Aircraft Temp Near IR Window Temp

32 Additional Satellite Signatures of Convectively- Induced Turbulence Transverse Bands 93% (46%) of transverse band cases featured light (moderate) or greater turbulence during summer 2006

33 Summary Initial convective storm growth signals in satellite imagery can precede significant radar echoes more than 1 hour UW-CIMSS and UAH have developed methods to objectively detect these initial convective storm growth signals in support of: 1) Operational weather forecasting, 2) Aviation weather nowcasting expert systems (CIWS, CoSPA, GTG-N), 3) The GOES-R ABI instrument program Lightning initiation most often occurs with rapidly developing storms and 10.8 micron BT between K - Cloud top cooling rates based upon box-averaging most often provide 1 hour lead time and POD of 40% and FAR of 22% for LI nowcasting 1) Current GOES nowcast products are daytime only, but day/night nowcast capability using cloud type/phase information is well underway to improve POD and FAR stats 2) Convective initiation, overshooting, gravity waves, and transverse bands are well correlated with turbulence for commercial aviation External collaborations (i.e. South African Weather Service) are important for evaluating algorithm performance for regions outside the U.S. - We would welcome European radar reflectivity and/or lightning detection data for use in satellite product validation