A n Objective Nowcasting Tool that Optimizes the Impact of Frequent Observations in Short-Range Forecasts a.k.a. “Stopping Short-Range Gap-osis” Ralph Petersen 1, Robert M Aune 2 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin – Madison, Madison, Wisconsin, 2 NOAA/NESDIS/ORA, Advanced Satellite Products Team, Madison, Wisconsin,

Basic Mantra: Good data should be believed, used and re-used Presentation Outline Basic Mantra: Good data should be believed, used and re-used Focus on 3-6 hour forecast time frame –Expand Forecaster’s tools –Compliment and Update conventional NWP guidance Address the problem of Detecting and Forecasting the Dynamical and Thermodynamical Forces which:  Create the pre-convective environment and  Help trigger convective initiation Develop techniques to Expand the utility of Existing and Future Satellite Products –Go beyond considering data useful only as observations –Apply the techniaues Over Land Combine satellite data with other data in daily forecasting –Maximize benefits of both

A basic premise - Any Nowcasting Model Should: Be used to update/enhance other numerical guidance: Be Fast Be run frequently Not needed to be constrained by longer-range NWP ‘computational stability’ issues Use all available data: “Draw closely” to good data Important for anticipating rapidly developing weather events: “Perishable” guidance products – rapid delivery Run Locally? – Few resources beyond comms, users easily trained We will focus on the “pre-storm environment” Increase Lead Time / POD and Reduce FAR Increase Lead Time / POD and Reduce FAR Goals: - Increase the length of time that forecasters can make good use of quality observations (vs. NWP output) for their short range forecasts - Provide objective tools to help them do this

Making GOES Sounder Derived Product Images (DPI) more useful to forecasters To increase usefulness, rather than viewing sets of individual profiles, GOES Sounder products are being made available to forecasters as images Products include: - Total Precipitable Water (TPW) - 3-layers PW - Stability Indices,... DPIs + Speed comprehension of information in GOES soundings, and + Improve upon Model First Guess, but Only used as observations, and - Have no predictive component - Data not used in any NWS NWP models GOES hPa Precipitable Water - 20 July 2005

Case of convection on 20 July 2005 which was initially moved over Central Wisconsin, Decayed and then Redeveloping past Chicago Why did initial convection decay and why did it reform where it did? 1545 UTC 1715 UTC 1915 UTC

Lower-tropospheric GOES Sounder Derived Product Imagery (DPI) 3 layers of Precipitable Water Sfc-900 hPa hPa hPa 0700 UTC GOES hPa Precipitable Water - 20 July UTC 1300 UTC 1800 UTC However, after initial storm has developed, cirrus blow-off masks lower-level moisture maximum in subsequent IR satellite observations Small cumulus developing in boundary layer later in morning also mask retrievals in second area. Moisture Maximum

0700 UTC 1000 UTC 1300 UTC 1800 UTC Mid-tropospheric GOES Sounder Derived Product Imagery (DPI) 3 layers of Precipitable Water Sfc-900 hPa hPa hPa Over time, cirrus blow-off also masks the presence of an extended area of mid-level dryness. (Differential motion of lower-tropospheric moisture under upper-tropospheric dryness creates convective instability) GOES hPa Precipitable Water - 20 July 2005 Maximum Dryness

1 - Can we retain the satellite observations that were ‘lost’ after the cirrus shield appeared following the initial storm development? 2 - Does the underlying premise of data assimilation (that data should make small changes to an already good model) hold for nowcasting, where small-scale data extremes observed by various different observing systems can make the difference between a good and bad forecast? Two basic questions:

For Nowcasting - Have we forgotten the Golden Rule of Forecasting? Always remember to look out of the window (or at satellite imagery) What do you see? - Clouds (or water vapor features) What are they doing? - Moving as ‘entities’ from one place to another The fundamental question to be addressed by this effort is: For Nowcasting, are there advantages in exchanging the non-linear advection terms in ‘classical’ grid-point models (which ‘recreate’ clouds as they move from grid point to grid point) with simpler LaGrangian schemes that can be used to update traditional NWP guidance by predicting the movement of parcels that are frequently updated and initialized at observation points.

LaGrangian Trajectories Clearly show Development of Interplay between Dry Line from West and Moist Gulf Flow from South  Forward Trajectories  Backward Trajectories Motivation for a LaGrangian Nowcast approach? Sample LaGrangian Diagnostic Study of development of Pre-Convective Environment using 3-hourly Radiosonde Data From SESAME From Kocin et al., LaGrangian Techniques formulations based on Greenspan’s Discrete Model Theory (1972,73)

0 hr LaGrangian & NWP hourly Acceleration-300hPa Numerical Considerations: Comparison of acceleration calculations for  LaGrangian (Parcel) vs. Eulerian (Grid Point)Models  Eulerian Accelerations are: -Smaller-scale & less-organized (and therefore larger spatial gradients and greater spatial/temporal variability) -Have 2-3 times greater maxima -Change greatly over time

NWP – 2 hr Wind Error – 300hPa Visually similar though not exact Pattern alike Magnitudes different A driver for: CFL stability, higher order (costly?) advection schemes, and fine grid spacing Eulerian Model  wind forecast errors Appear to be related to Inertial Advection terms 

- Data can be used directly without ‘analysis smoothing’ – Retains maxima, minima and extreme gradients - Can combine Wind / Moisture observations from two sources - Spatial resolution adjusts to available data density and dynamics - GOES sounder products can be projected forward at full resolution – even as they move into ‘data void’ cloudy areas - Use all data at time observed – not binned and averaged - Parcels from successive nowcasts can be tracked, aged and combined for output - Use best aspects of all available data sets e.g., Cloud Drift winds can be combined with surface obs cloud heights to create a ‘partly cloudy’ parcel with good height and good motion e.g., Wind Profiler / Aircraft data can be projected forward with accelerations What are the benefits of a LaGrangian (‘Parcel’) Nowcasting Model? - It is fast (15 min  t) and needs minimal computing resources - Can be used to ‘update’ other NWP guidance or ‘stand-alone’ DATA DRIVEN

Analytical tests simulating Dynamics of an Idealized Jet Streak Fully balanced parcels enter Jet Streak from west with various balanced flow speeds, Jet Streak magnitudes, Sizes and Motions.

Divergence/Convergence is not divided into four symmetrical quadrants. Divergence on the cyclonic side is offset slight ahead and left of the Geostrophic max and extends well ahead of the core on the anticyclonic side. Divergence fields are dominated by cross-stream flow components. Convergence in anticyclonic exit region aloft corresponds with “Dry Slots’ in WV imagery. Strong Divergence/Convergence transition ahead in area of (CAT,Gravity Waves) Analytical tests simulating Dynamics of an Idealized Jet Streak

Objective Nowcasting Development to date What are we trying to accomplish? Forecasters now use GOES imagery to monitor weather – and make Subjective forecasts into the future Increasing usefulness of Satellite data in operational Nowcasting We are developing tools to allow forecasters to: - Project detailed satellite products into the near future – objectively - Preserve observed extreme gradients and max/min features - Retain earlier IR information in areas where clouds develop - Provide rapid and timely updates to NWP guidance - Focus on weather events of greatest local need - Mix multiple data sources Why are we proposing to use a LaGrangian Approach for Objective Nowcasting?

1 - Document the approach 2 - Perform analytical accuracy and performance tests 3 – Developing a Prototype Objective LaGrangian Nowcasting Model Initial development focusing on the optimal approaches for providing updates of existing mesoscale model outputs in the free-atmosphere The objective of these tests is to provide forecasters 3 to 6 hour forecasts of the DPI fields updated every hour Combines strengths of Short-Range NWP with strength of Satellites - Uses winds from RUC-II analyses + RUC-II geopotential anal/fcsts - Matches these data with GOES Sounder (DPI) Water Vapor Data GOES Sounder profiles not currently used in RUC over land - Projects the data matches forward in time (at 15 minute intervals) Additional observations can be included as they arrive (Satellite updates every minutes possible) What have we done to date?

Returning to 20 July 2005 case Question: Can GOES Sounder Derived Product Imagery (DPI) be projected forward in time to provide forecasters more information about second storm development south of Chicago at ~ 1800 UTC? 0700 UTC GOES hPa Precipitable Water - 20 July UTC 1300 UTC 1800 UTC However, after initial storm has developed, cirrus blow-off masks lower-level moisture maximum in subsequent IR satellite observations Small cumulus developing in boundary layer also mask retrievals in second moist area. Moisture Maximum

0.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue 20 July 2005 Case Study Nowcasting the Lower-Tropospheric Moisture Source 1200 UTC data projected forward to 1800 UTC Blue = Moisture Maximum White =Cloudy

0.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

0.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

1.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

1.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

2.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

2.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

3.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

3.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

4.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

4.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

5.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

5.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

6.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue

6.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-10 mm – Red, mm – Green >20 mm - Blue Important Lower-Level Moisture Information about Storm Formation Obscured by Clouds from Storm in Observations

0.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue 20 July 2005 Case Study Nowcasting the Middle-Tropospheric Dryness 1200 UTC data projected forward to 1800 UTC Purple=Maximum Dryness White =Cloudy

0.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

0.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

1.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

1.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

2.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

2.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

3.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

3.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

4.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

4.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

5.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

5.50 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

6.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue

6.00 Hr LaGrangian Nowcast GOES Precipitable Water 0-5 mm – Purple, 5-10 mm – Black >10 mm – Light Blue Important Upper-Level Dryness Information about Storm Formation Obscured by Clouds from Storm in Observations

0.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple Sample Nowcasts from 20 July 2005 Case Study Combining Lower-Level Moisture and Middle-Tropospheric Dryness to Derive Convective Destabilization Overlays of 1200 UTC data projected forward to 1800 UTC

0.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

0.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

1.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

1.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

2.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

2.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

3.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

3.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

4.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

4.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

5.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

5.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

6.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

6.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

6.00 Hr LaGrangian Nowcast GOES Lower-Tropospheric Moisture - >20 mm PW - Blue GOES Upper-Tropospheric Dryness mm PW– Purple

1 - Document the approach 2 - Perform analytical accuracy and performance tests 3 - Develop Initial Prototype 4 - Initial testing --- Future work - Optimize wind level selection to match satellite channel weighting - Improve visualization tools to view predicted DPIs in formats identical to the observational products. Will require improvements to LaGrangian model, including ‘data aging’ and ‘continuous successive image merger’ algorithms - Integrate Profiler, Aircraft and cloud tracked wind data to provide observed wind as well as moisture data updates. - Test the impact of higher vertical resolution AIRS soundings in resolving the pre-convective environment. What are we doing next?

Using the trajectory procedure in both a Forward (forecast) and Backward (diagnosis) mode for OKC tornado case. -Wind Profiler data taken at 2100UTC (shortly prior to the first severe thunderstorm development) are both traced backward (to trace the origins of the air parcels) and forward (to project their future paths and indicate potential divergence) for 3 hours. Note: 1- Increase in convergence between 2100 and 0000 UTC and 2- Increased cyclonic curvature during the period of storm development, an area which 3 hours earlier had shown divergence. These results are consistent with diagnostic calculation of divergence and moisture flux divergence made using the Wind Profile and AERI data. Integrate Profiler wind data to provide observed wind

Incorporating of other data sources Although the Wind Profilers are land based, they provide an excellent source of wind data for calculating Moisture Flux Convergence when used in conjunction with co- located Water Vapor information over land from: - GEOS - AIRS - AERI - Some MDCRS aircraft - With Wind and Moisture One advantage of the ‘off-line, stations based’ system used in these tests is that it is very simple and PC based – thereby easily transferred to WFO use – and can be run in ‘true real time’ and refreshed whenever new data arrive

- Data can be inserted (and combined) directly without ‘analysis smoothing’ – retain maxima and minima Provide Forecast Imagery that are consistent with observations Summary – An Objective LaGrangian Nowcasting Model - Quick and minimal resources needed - Can be used ‘stand-alone’ or to ‘update’ other NWP guidance DATA DRIVEN Coordinate Independence - Initial tests done in pressure for convenience - Propose developing isentropic system - May be able to reduce dependence on ‘deep’ hybrid surface domain in current models -Can combined LaGrangian Dynamics with Eulerian surface model -Forward Data Projection techniques which preserve data and parcel accelerations may also benefit mesoscale data assimilation Goals: - Provide objective tools to increase the length of time that forecasters can make good use of dependable observations (vs. only NWP output) for their short range forecasts -Expand the use of GOES sounder products from subjective observations to objective nowcasting tools

Summary - A LaGrangian Nowcasting Model Can: Be used to update/enhance other numerical guidance: Be Fast Be run frequently Not constrained by longer-range NWP ‘stability’ issues Use all available data: “Draw closely” to data ( retain maxima, minima and extreme gradients ) Be useful in anticipating rapidly developing weather events: “Perishable” guidance products – rapid delivery Run Locally? – Few resources beyond comms, users easily trained Focus on the “pre-storm environment” Increase Lead Time / POD and Reduce FAR Goals: - Increase the length of time that forecasters can make good use of dependable data (vs. NWP output) for their short range forecasts - Provide objective tools to help them do this