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Tropical Weather States from the ISCCP D1 Cloud Dataset

Introduction

We extend to the whole tropics (±15° latitude) the analysis approach of Jakob and Tselioudis (2003, henceforth JT03) that uses the patterns of cloud property joint distributions at mesoscale (cloud type mixtures) to identify distinct states of the tropical atmosphere. This approach provides a more compact way to study multi-variate relationships among clouds, meteorology and energy-water exchanges (Rossow et al, 2005).

Data and Analysis Method

We look for distinctive patterns in the joint frequency distributions of the cloud top pressure (PC) and optical thickness (TAU) values from individual satellite image pixels (fields-of-view about 5 km in size) occurring within 2.5° regions (upper limit of the mesoscale) that are provided in the International Satellite Cloud Climatology (ISCCP) D1 dataset (Rossow and Schiffer 1999). The histogram patterns that describe cloud variability are identified using the K-Means clustering algorithm (Anderberg, 1973) applied to 3-hourly PC-TAU histograms for each 2.5° region, including completely clear regions.

To download the analysis software, click HERE.

Unlike JT03, the "best" number of clusters is determined objectively by repeating the analysis for an increasing number of clusters (starting at four, since JT03 tested 2-4 clusters) and judging the outcome by four criteria: (1) the resulting centroid histogram patterns must not change significantly (as judged by the pattern correlations among the centroids) when the random number initiating the analysis or the subset of data analyzed is changed, (2) the resulting centroid patterns should differ from each other significantly (pattern correlations should be low, usually < 0.6), (3) the spatial-temporal correlations of the centroid histograms should also be low, and (4) the distance between cluster centroids should be larger than the dispersions of the cluster member distances from the centroid. Tests illustrated below for K=5-7 showed that the results were unstable (violation of the first criterion) when the cluster number was too small (K < 6 in this case) and that, when the cluster number was increased, the patterns of the new clusters were still significantly different from each other (second criterion). When there were too many clusters (K > 6), two or more of them would have very highly correlated PC-TAU patterns, as well as highly correlated spatial distributions or time variability (usually all three). In particular, two of the K-cluster patterns are highly correlated with a single cluster pattern from the (K-1) analysis, indicating a splitting of that cluster. The last criterion is actually used in the analysis to optimize the cluster set, so it is always met; but a post-facto check showed generally decreasing dispersion-to-separation distance ratios as the number of clusters increases. For K = 6 the average dispersion-to-separation distance ratio is less than one half.

The optimum cluster centroids are identified for K=6 as shown above and reported below on the left. Next, each PC-TAU histogram for each 3-hr time step in each 2.5° map grid cell over the whole tropics is assigned to one of these clusters. We refer to these as "weather states" (WS) because JT03 showed that these cloud property patterns are associated with distinct states of the tropical atmosphere (Fu et al. 1990, 1994, Lau and Crane 1995, Del Genio and Kovari 2002). Variations of WS can then be represented by the frequency of occurrence maps for daily or longer time accumulations shown below.

We also define frequency of occurrence distributions for larger regions, 30°×30° latitude-longitude (beginning at 0E longitude), accumulated for daily or longer time periods (completely clear regions are counted as a 7th frequency category). To examine the time variations of the WS frequencies of occurrence, we used a wavelet (Morlet mother wave, Torrence and Compo 1998) analysis.

Wavelet Analysis Results

Time Series* and Wavelet Spectra†

Select a weather state     and longitude band

* Time series smoothed to 1-week resolution.
† Wavelet software was provided by C. Torrence and G. Compo, and is available at URL: paos.colorado.edu/research/wavelets/.

The Tropical cluster dataset is available for download. Data is written with a field width of 3 characters. The D1 data is gridded on a 2.5×2.5° equal-angle (square) global grid. The D1 Tropical cluster subset grid is 144×12 (longitude×latitude) and has a total of 1728 cells. Longitude varies first, and begins at 0°E and proceeds eastward to 360°E in increments of 2.5 degrees. Latitude begins at 15°S and proceeds northward to 15°N in increments of 2.5 degrees. The first record of the Tropical cluster dataset corresponds to 7/1/1983, UT0000 and the records proceed in 3 hourly increments to 12/31/2004, UT2100. There are 7855 days total in the time period studied, resulting in 62840 [(7855 days)×(8 observations/day)] records in the D1 Tropical cluster dataset.

Note: Weather state values range from 1-8. In the cluster datasets, the value 0 indicates Clear; value 98 indicates darkness; and value 99 indicates missing data.

To download the datasets, click here (file D1.WS.Tropics.1.dat).

PLEASE NOTE — An error in the first version of the Tropical cluster dataset, which caused data from 3/1/1988-12/31/1988 to be repeated from 3/1/1989-12/31/1989, was discovered. The dataset has been replaced. The current version is D1.WS.Tropics.1.dat.

References

  • Anderberg, M.R. (1973), Cluster Analysis for Applications, 359 pp., Academic Press, New York, NY, USA.
  • Del Genio, A.D., and W.K. Kovari (2002), Climatic properties of tropical precipitating convection under varying environmental conditions, J. Climate, 15, 2597-2615.
  • Fu, R., A.D. Del Genio and W.B. Rossow (1990), Behavior of deep convective clouds in the tropical Pacific deduced from ISCCP radiance data, J. Climate, 3, 1129-1152.
  • Fu, R., A.D. Del Genio and W.B. Rossow (1994), Influence of ocean surface conditions on atmospheric vertical thermodynamic structure and deep convection, J. Climate, 7, 1092-1108.
  • Jakob, C., and G. Tselioudis (2003), Objective identification of cloud regimes in the Tropical Western Pacific, Geophys. Res. Lett., 30, 1-4, DOI:10.1029/2003GL018367.
  • Lau, N-C., and M.W. Crane (1995), A satellite view of the synoptic-scale organization of cloud properties in midlatitude and tropical circulation systems, Mon. Wea. Rev., 107, 1984-2006.
  • Rossow, W.B., and R.A. Schiffer (1991), ISCCP cloud data products, Bull. Amer. Meteor. Soc., 72, 2-20.
  • Rossow, W.B., Tselioudis, G., Polak, A., and Jakob, C (2005), Tropical Climate Described as a Distribution of Weather States Indicated by Distinct Mesoscale Cloud Property Mixtures, Geophys. Res. Lett., 32.
  • Torrence, C., and G.P. Compo (1998), A practical guide to wavelet analysis, Bull. Amer. Meteor. Soc., 79, 61-78.

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