Development of Severe Weather Products for the GOES-R Advanced Baseline Imager Introduction The Advanced Baseline Imager (ABI) aboard the GOES-R series will have a number of additional spectral channels at significantly improved spatial and temporal resolution compared to the current GOES Imager. This wealth of data likely contains some useful information about the pre-storm environment as well as active thunderstorms. In designing new algorithms to extract this information, one must find the most suitable combination of bands. This poster describes how we take simulated ABI imagery at the proper spatial and temporal resolution and develop products meant to improve severe weather nowcasting and forecasting. One method for determining the relative importance of each spectral band is called Principal Component (PC) Analysis. PC imagery highlights areas which explain the most variance in the data, and the corresponding eigenvectors can be used to determine what linear combination of spectral bands is most suitable for studying certain features. For example, in looking at low-level moisture depth, PC Analysis has revealed the 11.2, 12.3, and 13.3 µm bands each provide some information, but the signal is stronger in some bands than others. We plan to use a similar analysis to develop a number of severe weather products. Studies of this nature are important in preparation for the launch of GOES-R. Daniel T. Lindsey* and Don Hillger NOAA/NESDIS/STAR/RAMMB Louie Grasso Cooperative Institute for Research in the Atmosphere Principal Component Imagery PC Imagery has been used in the past with both geostationary and polar orbiter data to assist with feature identification (e.g., Hillger and Clark 2002). The process for calculating the PC images is as follows: Build a matrix A which has ‘m’ rows, corresponding to the number of pixels in the image, and ‘n’ columns corresponding to the number of spectral bands. Each element in the matrix is an observation (a brightness temperature, for example) at a given pixel for a particular band. Then calculate a covariance matrix of A using C = 1/m A T A in which the diagonal elements represent the variance of the respective spectral bands. Next, compute the eigenvalues and eigenvectors of C (there will be ‘n’ eigenvectors). Finally, take the original data matrix, A, and multiply by each eigenvector to get the corresponding PC Image vector. This vector will have length ‘m’, and represents a map in which each element is a pixel. It should be no surprise that PC 1 (Fig. 3) varies in the same direction as the brightness temperatures. In this case, it is more interesting to look at the situation in which only the moisture depth varies (Fig. 4), because that’s the quantity we’re attempting to retrieve. The corresponding eigenvectors show that the band explaining most of the variance is the 12.3 µm band, followed by 13.3 µm and 11.2 µm. This result can also be seen by noting that 12.3 µm brightness temperatures have the largest east-west range than at any other wavelength. In other words, the weighting function at 12.3 µm likely peaks closest to the boundary layer moisture, and this channel should prove most valuable in a retrieval. In practice, forming a difference between a band having a relatively large amount of water vapor absorption with another having little will be necessary. This is an attempt to subtract out the surface temperature effect. We therefore form three test brightness temperature combinations: µm minus 12.3 µm (this is similar to the traditional “longwave difference” product), and two linear combinations of all 5 channels using coefficients resulting from the PC analysis. The details of these linear combinations are excluded due to space limitations. We arbitrarily define “moisture depth” as that vertical level in which the water vapor mixing ratio first drops below 2.5 g/kg. Points are randomly sampled throughout the domain in Fig. 1, and the results are plotted in Fig. 5. Although difficult to see, the largest slope and smallest variance occurs with the traditional longwave difference product. This may be due to the fact that adding additional channels only adds more noise to the retrieval. References Grasso, L., M. Sengupta, and D. T. Lindsey, 2008: Synthetic GOES-R imagery development and uses. 5 th GOES User’s Conference, New Orleans, LA, January 2008, P1.19. Hillger D. W., and J. D. Clark, 2002: Principal component image analysis of MODIS for volcanic ash. Part II: Simulation of current GOES and GOES-M imagers. J. Appl. Meteor., 41, 1003– *Corresponding author address: 1375 Campus Delivery, CIRA/CSU, Fort Collins, CO, P1.78 Application to a Boundary Layer Moisture Depth Retrieval As a first step in developing GOES-R severe weather products, we will attempt to use PC Imagery to assist in the creation of a boundary layer moisture depth retrieval. Moisture depth is an extremely important factor in severe weather forecasting and nowcasting, particularly in the central and high plains where thin moisture will often mix out with afternoon heating. Numerical models generally do a poor job predicting mix-out, and existing water vapor products, such as Precipitable Water (PW) and GOES Sounder point retrievals, do not focus on the important layer from just above the surface to the top of the boundary layer. Water Vapor Depth Sectors: ˚C 19˚C Fig. 1. Simulated µm band over a sector in which boundary layer moisture depth increases from west to east, and surface temperature increases from north to south. The largest and smallest brightness temperatures are denoted on the image for reference, and the gray shades represent the remaining brightness temperatures. Fig. 2. Water vapor mixing ratio profiles for 4 selected N-S-oriented sectors shown in Fig. 1. Sectors in the east have deeper boundary layer moisture than those to the west. An idealized sector was created in which the boundary layer moisture depth increases from west to east and the boundary layer temperatures increase from north to south. The temperature within the boundary layer was made to decrease with height at the same lapse rate at every location. An observational operator (Grasso et al., poster 1.19 in this session) was then used to simulate GOES-R µm brightness temperatures; the resulting image is shown in Fig. 1 (the map is arbitrary and is only drawn to provide an approximate horizontal scale). Fig. 2 shows water vapor mixing ratio profiles in the lowest 4 km from four of the sectors in Fig. 1. Next, PC Imagery was calculated using 5 bands from the GOES-R ABI: 8.5 µm, µm, 11.2 µm, 12.3 µm, and 13.3 µm. Since brightness temperatures increase from southwest to northeast in each of the 5 bands, the first PC represents 97% of the variance; it is shown in Fig. 3. Fig. 4 shows the first PC of a sector in which the temperature profile is held constant throughout the domain, but boundary layer moisture depth still varies from west to east. Fig. 3. PC 1 using the sector described in Figs. 1 and 2. Fig. 4. PC 1 using a sector in which only boundary layer moisture depth varies. Future Work The goal of this poster is simply to demonstrate how PC Analysis might be applied to simulated GOES-R ABI Imagery in order to develop future severe weather products. The moisture depth retrieval is a work in progress, but these results will be very useful in evaluating the best band or bands to use. As a next step, we plan to use the current GOES-11, which has a band centered near 12.0 µm, to calculate the longwave difference at the same time and place as raobs are launched (if skies are clear) in order to determine statistically whether a boundary layer depth retrieval is feasible. Fig. 5. Brightness Temperature Differences vs. boundary layer moisture depth using 3 different channel combinations, from the domain shown in Fig. 1. Acknowledgements The views, opinions, and findings in this report are those of the authors, and should not be construed as an official NOAA and or U.S. Government position, policy, or decision.