Validation Exercise

1. Validation Exercise First: Where is the GDI expected to work? GDI-BT r^2

2. In areas there the ECI is high. These often exclude cold current environments and areas that are too cool and dry such as the extratropics Summer ECI High in Summer In areas where the II is has negative values. These are regions where trade-wind-type subsidence inversions play an key role in convective development. Subtropical Latitudes are best Best Best GDI best in dark green areas Trade-wind-type inversions Trade-wind-type inversions ECI is high in the Tropics Good Best in Trade Wind Regimes and east coasts Good Best in Trade Wind Regimes ECI High in the Tropics Summer Fair GDI sensitive to changing mid-level temperatures which reflect in the MWI. These occur whenever cold/warm temperatures associated with ridges/troughs stabilize/destabilize the low-mid troposphere. GDI best in dark green areas Best GDI best in dark green areas Best Subtropical Latitudes are best Summer Summer Where is the GDI expected to work best? • Recall driving processes: • ECI: High-THTE column in the • low-mid troposphere • (2) MWI: Mid-level ridge stabilizing • (3) II: Subsidence inversions causing • low-level stabilization + drying

3. Is this what we found? Summer GDI-BT r^2 Best Best Good Good Fair Best Best Summer GDI-OLR r^2

4. Validation Exercise North America • GDI vs Brightness Temperature: Index values from 1x1 deg GFS00 compared against IR4 Brightness Temperatures from GOES. Each value represents a 12-hour average (GFS00 F00-F12, GOES 00z-12z) • Timeframe: July-November 2013 (Wet season) South America • GDI vs OLR: Index values from 1x1 deg GFS compared against OLR. Each value represents a 24-hour average (GFS00 F00-F24, OLR 00z-00z_day+1). • GDI vs Rain: Index values from 1x1 deg GFS compared against 24-hr rainfall analyses from CPC. Each value represents a 24-hour average (GFS00 F12-F36, OLR 12z-12z_day+1) • Timeframe: October 2013-February 2014 (Wet season) • Both continents in the respective rainy seasons • Southamerica had more thorough analysis thanks to Silvia and the rest of the team.

5. Dataset limitations CPC Rain -Stations density very low in some places -Convective rainfall can be very localized and not being measured. OLR and -Assumption that temperatures/OLR Brightness measurements are representative. Temperature -Cirrus contamination, especially under subtropical jets and around MCSs Index -Dependent on model output. If the model Values is wrong then all indices are.

6. Validation philosophy for OLR/BT Pixel value represents the convective regime. As values decrease the convective regime increases in intensity. Some regimes are equivalent such a numerous shallow cells versus a few isolated deep cells. Validation Philosophy Validation question: How well does the GDI capture area coverage and type of convection (shallow vs deep)? -We are interested more in this than rainfall amounts. This also reflects better when validating with BT or OLR.

7. Results in the Caribbean! GDI correlates well with brightness temperature – for the most part. This means that in areas such as the Gulf of Honduras the GDI can diagnose 50% of the variance of the convective regime. Scatterplots coming later…

8. GDI - K The correlations were better than those of the K! The GDI tends to work better! Comparison against other stability indices • r2 of the GDI were compared • to those of other indices. • Question: “How much better • does the GDI resolve the • potential for convection?” • Red means the GDI did • better than the other index GDI - LI GDI - CAPE GDI - TT

9. Comparison against Precipitable Water (PWAT) GDI is best where convection alternates between shallow and deep. ITCZ: Deep convection too persistent. PWAT is not a stability index but is a good indicator of the amount of water vapor available for precipitation. r2 GDI - PWAT

10. Why does the GDI compete with PWAT? • Lets revisit the large scale transport of heat and moisture. • PWAT reflects the deep-layer moisture transport. • Is the vapor integrated throughout the troposphere. • The GDI also captures this transport (for the most part). Yet • since it considers inversions and troughs/ridges, the GDI • does a better job defining more discrete regions with • convective instability. • PWAT, as the K does, has problems differentiating between • shallow convection regimes, overestimating the potential. • It is easier to identify regions with the potential for • convection with the GDI than with PWAT.

11. Scatterplots! Brightness T vs Index Value Mostly Deep (-35C to -5C) Mostly Deep. Wide range, dynamics important (-40C to 0C) Wide range. Dynamics important. Often deep (-30C to +5C) Wide range. Dynamics important. Often deep (-25C to +5C) Few Deep (-5C to +15C) Few Deep (-5C to +15C) Mostly shallow (+5 to +20C) Shallow? Shallow (+10 to +25C) r2>0.5 r2~0.45 r2~0.4 r2~0.27 Index Values and Expected Convective Regime >40 High chance for deep cells High chance for deep cells High chance for deep cells High chance for deep cells 30-40 Mostly Deep (-40C to -5C) Mostly Deep (-30C to -10C) 20-30 Many Deep (-25C to +5C) Many Deep (-25C to 0C) 10-20 Some Deep (-15C to +10C) Wide range, dynamics important. Some Deep (-15C to +5C) 0-10 Mostly shallow (+5 to +20C) Wide range, dynamics important. A few deep (-5 to +20C) <0 Shallow (+10 to +25C) Shallow (+10 to +25C)

12. Two useful applications • Forecasting of type of convective regime expected especially in tropical and subtropical regions. To some extent, expected rainfall amounts can be inferred from convection type.  Great to identify potential for thunderstorms (aviation) as it resolves finer structures.  Independent – to some extent – of convective parameterization. 2) Find and track perturbations in the trades Captures convective instability associated with Tropical, Easterly and TUTT-induced waves. Also captures weaker perturbations that produce enhanced shallow convection.

13. Tracking waves in the trades with the GDI

14. Tracking waves in the trades with the GDI

15. Summary (1) Up to now the GDI seems to be the best available tool to evaluate the potential for tropical convection. Works best in trade wind regimes and in areas downwind. +Here, important processes aside from heat and moisture availability are: -Subsidence Inversions -Mid-level troughs and ridges. (2) Beats the K-index across most of the Caribbean & South America. (3) Only competes with PWAT but captures much better the structure of convection. (4) As forecast time increases, the GDI can beat model rainfall as a predictor since it is – to some extent – independent of the convective parameterization. (5) Best way to use the GDI: Look at the GDI, PWAT and flow. Dynamics are very important. (6) Wide range of significant applications: Aviation (potential for t-storms); general forecasting of tropical convection; monitor trade wind perturbations.

16. More information at… http://www.wpc.ncep.noaa.gov/international/gdi/ • Realtime 7-day GDI forecasts using GFS model output. • More information, including documents and package to • install the GDI if you are a Wingridds user. Contacting us… jose.galvez@noaa.gov Questions?