NWS Training Slide Set John R. Mecikalski, UAH 1 Automated Geostationary Satellite Nowcasting of Convective Initiation: The SATellite Convection AnalySis and Tracking (SATCAST) System John Mecikalski 1 Kristopher Bedka 2 1 University of Alabama-Huntsville 2 Cooperative Institute for Meteorological Satellite Studies (CIMSS), UW-Madison NWS Training Slides Prepared: 6 August 2006

NWS Training Slide Set John R. Mecikalski, UAH 2 Motivation Numerical models have significant problems “ nowcasting ” location/intensity of convective weather phenomena in the 0-6 hour time frame This is especially true over oceanic regions where poor initialization results in incorrect location/intensity forecasts for convective storms Since little real-time satellite-derived data is available in airplane cockpits, coupled with NWP deficiencies, mid-flight convective storm initiation and growth represents a significant hazard for aviation interests A major portion of the accidents from aircraft turbulence encounters are within close proximity to atmospheric convection (Kaplan et al, 1999) The cost of diverted flight can be as high as $150,000 and a cancellation close to $40,000, depending on the size of the plane (Irrgang and McKinney, 1992)

NWS Training Slide Set John R. Mecikalski, UAH 3 The NASA sponsored Advanced Satellite Aviation weather Product (ASAP) initiative was started to better infuse satellite data into FAA Aviation Weather Research Program (AWRP) product development teams' (PDT's) aviation weather diagnostics and forecasts Geostationary satellites provide excellent coverage (both spatial and temporal) of regions prone to convective storms (60° S – 60° N) - Since one can see the development of convection in satellite imagery, we sought to develop an algorithm to identify pre-convective initiation signatures and nowcast new convective initiation in real-time - Convective Initiation: The first detection of significant precipitation echoes (> 30 dBz) from cumulus clouds by ground-based radar Motivation (cont’d)

NWS Training Slide Set John R. Mecikalski, UAH 4 Datasets USE McIDAS to acquire and process: GOES-12 1 km visible and 4-8 km infrared imagery every 15 mins - CI nowcasting techniques can be applied to any high-resolution (≤ 4 km) geostationary satellite sensor where satellite-derived winds are available - IR data interpolated to the 1 km visible resolution for direct relationship between IR and VIS analysis techniques UW-CIMSS visible/IR satellite derived winds for cloud motion assessment - Winds used to track cumulus features back in time for cloud-top trend estimates WSR-88D base reflectivity composite used for real-time validation - Composite also interpolated to the 1 km VIS resolution (not shown)

NWS Training Slide Set John R. Mecikalski, UAH 5 Evaluation of Pre-CI Satellite Signatures Integrate GOES satellite and WSR-88D radar imagery - Identified GOES IR T B and multi-spectral technique thresholds and time trends present before convective storms begin to precipitate - Studied numerous real-time and archived convective events with diverse mesoscale forcing regimes and thermodynamic environments [continental (U.S. Great Plains) to sub-tropical (S. Florida)] - Leveraged upon documented satellite studies of convection/cirrus clouds [Roberts and Rutledge (2003), Ackerman (1996), Schmetz et al. (1997), Inoue (1987)] - After pre-CI signatures are established, test on other independent cases to assess algorithm performance

NWS Training Slide Set John R. Mecikalski, UAH 6 CI Interest Field Criteria CI Interest FieldCritical Value 10.7 µm T B (1 score) < 0 ° C 10.7 µm T B Time Trend (2 score) < -4 ° C/15 mins < -2 ° C/5 mins (GOES-11) ∆ T B /30 mins < ∆ T B /15 mins ∆ T B /10 mins < ∆ T B /5 mins (GOES-11) Timing of 10.7 µm T B drop below 0 ° C (1 score) Within prior 30 mins Within prior 10 mins (GOES-11) 6.5 (or 6.7) µm difference (1 score) -35 ° C to -10 ° C µm difference (1 score) µm difference -25 ° C to -5 ° C -3 ° C to 0 ° C (GOES-11) 6.5 (or 6.7) µm Time Trend (1 score) > 3 ° C/15 mins µm Time Trend (1 score) µm Time Trend > 3 ° C/15 mins > 1 ° C/5 mins (GOES-11) From RR03

NWS Training Slide Set John R. Mecikalski, UAH 7 May 4, 2003 Convective Event Slow-moving spring storm produced 90 tornadoes across Kansas, Missouri, Tennessee, and Arkansas Western KS and NE convection produced mainly wind/hail damage

NWS Training Slide Set John R. Mecikalski, UAH 8 Convective Cloud Mask The foundation of the CI nowcast algorithm…only calculate IR fields where cumulus are present Utilizes time of day/year dependent brightness thresholding, brightness gradients, and brightness standard deviation techniques Collaboration with Dr. Udaysankar Nair (UAH) to implement statistical pattern-recognition based cumulus detection method by summer 2004

NWS Training Slide Set John R. Mecikalski, UAH 9 Multi-Spectral Band Differencing Compared multi-spectral techniques with co-located WSR-88D imagery to identify difference thresholds for cumulus in a pre-CI state technique for cloud-top microphysics (Ellrod: WF 1995, Setvak and Doswell: MWR 1991) not used due to variation of 3.9 µm radiance with solar angle

NWS Training Slide Set John R. Mecikalski, UAH 10 Roberts and Rutledge, Weather and Forecasting (2003) *B”By monitoring via satellite both the cloud growth and the occurrence of subfreezing cloud-top temperatures, the potential for up to 30 min advance notice of convective storm initiation (> 35 dBz), over the use of radar alone, is possible” “Per-Pixel” Cloud-Top Cooling Estimates Study of colocated GOES µm T B and radar reflectivity pixel trends for stationary convective clouds along the Colorado Front Range Found that - 4°C/15 mins (- 8°C/15 mins) corresponds to weak (vigorous) growth 15 min ΔT B

NWS Training Slide Set John R. Mecikalski, UAH 11 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 T b = - 50°C T b =20°C Current Per Pixel Differencing  T b = 60°C  T b = - 70°C  T b = - 10ºC SOV Differencing  T b = - 10ºC T b = - 40°C T b =20°C t-15 mins ~1 km Satellite-Derived Offset Vector (SOV) Technique 10 ms -1

NWS Training Slide Set John R. Mecikalski, UAH 12 Satellite-Derived Wind Analysis 850 hPa Analysis (winds in kts) 4 images at 15 min frequency used for winds: Visible, 6.5 µm, and 10.7 µm - Reduced effect of NWP model background to better capture unbalanced mesoscale flows (i.e. anvil expansion, lower tropospheric outflow boundaries) Barnes analysis used to interpolate winds to ~1 km visible resolution - Wind field over 3 layers established ( , , hPa); height assignment based on 10.7 µm T B and NWP model temperatures

NWS Training Slide Set John R. Mecikalski, UAH 13 Cloud-Top Cooling Estimates: Moving Cumulus 1930 UTC2000 UTC

NWS Training Slide Set John R. Mecikalski, UAH 14 All CI Interest Fields10.7 μm Fields OnlyNo Anvil CI Nowcast Algorithm Nowcasts captured convective development well across eastern and north-central Kansas Conservative cloud growth threshold (4° C/15 mins) can lead to greater false alarm occurrences Detailed analysis reveals lead times up to 45 mins 2000 UTC2030 UTC2100 UTC CI Threshold

NWS Training Slide Set John R. Mecikalski, UAH 15 Since 5 min GOES-11 data was used, time trend thresholds are cut in half, resulting in noisy nowcasts for quasi-stationary convection in New Mexico TX Panhandle/OK convective development captured well CI Nowcast Algorithm: June 12th IHOP 2030 UTC2100 UTC2130 UTC

NWS Training Slide Set John R. Mecikalski, UAH 16 CI Nowcast Algorithm: August 3, UTC1745 UTC 1815 UTC Complex convective forcing from upper-level cold core cyclone, combined with lake breeze circulation Although noisy at first glance, CI over central/western IL identified up to 1 hour in advance Objective validation methodology very difficult to develop

NWS Training Slide Set John R. Mecikalski, UAH 17 Conclusions Through:1) identification of VIS cumulus clouds, 2) calculation of IR multi-spectral techniques, 3) tracking of cumulus cloud movement, and 4) estimation of IR cloud-top time trends, We have demonstrated skill in nowcasting CI and identifying growing cumulonimbus at min lead times using current generation geostationary imagery Mecikalski, J. M., and K. M. Bedka: “Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery”. Submitted to “IHOP_2002 Convective Initiation Special Issue” of Monthly Weather Review, April Hyperspectral satellite data will provide an unprecedented resource for: 1) characterizing the 3-D thermodynamic environment near air-mass/mesoscale boundaries 2) identifying pre-CI signatures for moving cumulus 3) diagnosing the intensity/severity of existing convective storms

NWS Training Slide Set John R. Mecikalski, UAH 18 Validation Method A: Non-objective Visual Comparsion 2000 UTC 2030 UTC 2100 UTC CI Threshold Very Good Moderate Poor  A visual comparison of the CI nowcast to future radar imagery would likely yield the “qualitative” skill descriptions provided above  Although this method may be “good enough” for most users (e.g. operational forecasters), people always want to know exactly how good the product is (e.g. correct nowcasts of CI occurrence 82% of the time)

NWS Training Slide Set John R. Mecikalski, UAH 19 Validation Method B: Objective Tracking of Radar + Satellite Step #1: Radar Tracking Algorithm 2000 UTC2030 UTC  Use sequential radar imagery from t to t+30 mins (hopefully with greater than 30 mins resolution) to determine where radar echoes moved for the 30 min period after the nowcast was made Step #2: CI Nowcast Pixel Advection  Go back to the CI nowcast (at 2000 UTC in this case) and advect flagged (red) pixels forward along the radar motion vector  Identify the radar reflectivity at the nowcast time and at the new location 30 mins in the future  LLook for dBZ increases from below 30 dBZ to above 30 dBZ. These are “good” CI nowcasts 2000 UTC