Satellite-based Convective Initiation Nowcasting System Improvements Expected from the MTG FCI Meteosat Third Generation Capability.

Transcription

1 Satellite-based Convective Initiation Nowcasting System Improvements Expected from the MTG FCI Meteosat Third Generation Capability Final Report EUM/CO/07/ /JKG Technical Report John R. Mecikalski The University of Alabama in Huntsville Huntsville, Alabama, USA 14. December 2007

2 Contents Executive Summary 3 1 Project Goals 3 2 Satellite Nowcasting Algorithm 4 3 Benefits of the FCI (and IRS) for Satellite Nowcasting of CI and Lightning General Enhancements for the MB06 Algorithm Enhanced Microphysical Monitoring Improved Atmospheric Moisture and Temperature Information Aerosol Information for Rainfall Initiation 11 4 Analysis of MSG during COPS for Infrared CI Interest Field Definitions 11 5 Outlook 15 References List of Figures List of Tables

3 Executive Summary This document outlines the improved capabilities that are expected from the Meteosat Third Generation Flexible Combined Imager (MTG FCI) for convective initiation (CI) and lightning initiation (LI) nowcasting (0-1 h forecasts). The CI and LI nowcasts are provided by an entirely satellite-based algorithm as originally developed for use with the Geostationary Operational Environmental Satellite (GOES) series of meteorological satellites. Additionally, information will be provided on how the MTG InfraRed Sounder (IRS) will offer significant improvements to such nowcasts given its anticipated hyperspectral-based soundings of temperature and moisture it will provide. All of these added capabilities are in comparison to the Imager currently aboard the Meteosat Second Generation (MSG) satellite. There are ~12 advancements the added channels and sounding abilities of the FCI and IRS will provide, and these are outlined herein. Use of Meteosat Second Generation (MSG) data from the Convective and Orographically-induced Precipitation Study (COPS) field experiment in 2007 provides a first look at the expected advantages of FCI for nowcasting CI and LI. 1. Project Goals The goals of this project are to demonstrate the added capabilities the FCI (and IRS) will offer to CI and LI nowcasting (0-1 hour forecasting), above those currently available on Imager aboard the Meteosat Second Generation (MSG) satellite. The improvements will come in three main areas: (a) an improved ability to monitor cloud-top (and infer incloud) microphysics as important to the precipitation-generation process; (b) an enhanced ability to monitor atmospheric stability and inversions that subsequently offer signs of pending CI and the likelihood that a cumulonimbus will produce lightning (and henceforth, the amount of lightning that could accompany a thunderstorm); And (c) an enhanced ability to monitor boundary layer aerosols, which have an affect on precipitation generation within newly formed cumulonimbus clouds. Geostationary (i.e. center over the Equator, and rotating with Earth s velocity at a distance of ~36,000 km) satellite data can be used to monitor clouds and their properties every 15 mins. As a result, one can measure the behavior of those cloud types that grow into thunderstorms. Specifically, cumulus clouds represent the rising warm air from the surface, and as these clouds grow in height, they cool (as the atmosphere cools with increasing altitude). Satellites measure such cooling with time, and therefore can estimate clouds growth. Satellites also estimate other properties of clouds, such as their width, microphysical (ice versus water) character, and thickness. Accumulating all of these aspects of growing cumulus, one can easily tell which clouds will become a thunderstorm within the coming min, and which will not. It is this capability that we wish to integrate into a system than can alert people to the coming hazards associated with thunderstorms, namely, heavy rain, lighting and strong winds. In effect, geostationary 3

4 satellite data can provide a significant lead on the early warning of hazardous weather from thunderstorms, well before the traditional radar systems can attempt. 2. Satellite Nowcasting Algorithm In the context of this discussion, convective initiation (CI) can be defined as the first appearance of a radar echo 35 dbz within a growing cumulus (Roberts and Rutledge 2003; Mecikalski and Bedka 2006; Mecikalski et al. 2007). The Mecikalski and Bedka (2006; here after MB06 ) CI methodology offers a sophisticated, satellite-based approach for real-time detection and monitoring of early convection of scales 1 km over large geographical regions (e.g., the continental US portion of a GOES (the US geostationary satellite) scan, with extension to cover the entire Gulf of Mexico, and across Central America). The algorithm developed in MB06 is strongly satellite based (needing numerical weather prediction fields only to assist in tracking the clouds via a atmospheric motion vector algorithm). These satellite-only convective fields need to be merged into existing systems that capitalize also on other atmospheric information (from models, radar, etc.). Results to date show considerable skill, up to ~98% probability of detection (POD), while false alarm rates (FAR) are around 60%, however FARs may be as low as 30% when specific GOES infrared interest fields are used (Mecikalski et al. 2008). All skill scores apply to CI nowcasting in the 30 min 1-hour period as identification of first-time thunderstorm development using this algorithm. The MB06 CI nowcasting method involves a simple summation of the number of interest fields satisfied per pixel. CI Interest Field Critical Value [1] 10.7 µm T B [IF1] < 0 C [2] 10.7 µm T B Time Trend < 4 C/15 mins [IF2, IF3] T B /30 min < T B /15 min [1] 10.7 µm T B drop to <0 C Within prior 30 mins [IF4] [1] µm difference 35 C to 10 C [IF5] [1] µm difference 25 C to 5 C [IF6] [1] µm Time Trend > 3 C/15 mins [IF7] [1] µm Trend > 3 C/15 mins [IF8] [8] CI Indicators GOES [1] 1.6 µm (for microphysics) [1] ~3.7 µm see D. Rosenfeld [1] ~ µm difference >0 C [1] µm difference >0 C [4] CI Indicators MODIS Figure 1: Tendencies and values 8 CI interest fields (bracketed numbers) used for GOES and MODIS data within the Mecikalski and Bedka (2006) algorithm. The Table (right) lists the critical values for evaluating CI within IR data. Note that MODIS is not an operational instrument, and therefore these are very preliminary and need to optimally developed using MSG or MTG. 4

5 Figure 2: GOES-12 convective initiation nowcasting over a Storm Prediction Center severe storm risk region. These products capture the initial development of a line convective storms across Iowa with few false alarms. Nowcasting confidence is estimated through the cloud-top cooling rate product, where rapid storm growth is assigned the highest nowcasting confidence level. The MB06 methodology makes use of a number of separate, satellite-based interest fields from GOES that are combined into a single, integrated, CI indicator. In this method, cumulus clouds are identified and tracked, using satellite-based mesoscale atmospheric motion vectors (MAMVs) designed to capture mesoscale flows (Bedka and Mecikalski 2005; Bedka et al. 2007), while the IR characteristics of the clouds are monitored [e.g., the 10.7 µm temperature (TB) and the µm TB differences; see Ackerman 1996; Schmetz et al. 1997]. Clouds of various types are identified using a 5

6 statistical clustering methodology (Nair et al. 1998; Berendes et al. 2007), as well as a separate cumulus cloud mask (CCM), which allows only cumulus to be monitored within the CI algorithm. Ultimately, cumuli meeting several cloud-top cooling and growth criteria are identified as being likely to produce a 35 dbz radar echo over the next minutes. Figure 1 shows the eight interest fields currently used from GOES, and the criteria that define a score on the 1-km pixel scale. As of Spring 2006, this CI algorithm is being run on a real-time basis over the Midwest U.S., the Southeastern quarter of the U.S., and over all of Central America. Figure 2 shows an example of this method's predictive skill for a case event highlighted in a Storm Prediction Center slight risk outlook for severe weather. Recent research demonstrates via principal component analysis (PCA) that all eight IR indicators (Figure 1-right for GOES) contain important information for nowcasting CI using GOES. Use of linear discriminant analysis (LDA) has led to an ability to estimate the increase in rainfall intensity over the next 1 hour. This later development extends the CI algorithm toward quantitative precipitation estimate (QPE) nowcasting in the 0-1 hour timeframe (Mecikalski et al. 2006). Table 1: Summary of visible and infrared channels data that the MSG SERVIRI and MTG FCI and IRS instruments can provide for CI and LI nowcasting (0-1 h forecasting). Here, HRV is high-resolution visible data. See text for descriptions on how these channels may be specifically used in the MB06 algorithm. The new MTG channels, above those from MSG, are highlighted in grey. Channels for CI and LI Nowcasting MSG HRV, 1.6, 3.8, 6.2, 7.3, 8.7, 10.8, 12.0 and 13.4 µm MTG HRV, 0.444, 0.96, 1.375, 1.6, 2.26, 3.8, 6.2, 7.3, 8.7, 10.5, 12.0 and 13.4 µm; IRS-retrieved soundings of temperature and moisture. The algorithm is being developed for nighttime CI forecasting as an extension of current capabilities, an important prelude to nighttime CI forecasting. Understanding the mesoscale dynamic forcing that maintains thunderstorm development when solar heating is absent is critical. These include low-level jets, decoupled atmospheric boundary layers, outflow boundaries, and moisture transport (e.g., Raymond 1978; Kessler 1987). Evolving the daytime CI satellite-based algorithm to a full 24-hour analysis system is underway, which involves developing new approaches for identifying CI/cumulus clouds at night when visible data are not available. Satellite analysis of a number of nighttime CI events, coupled with NWP simulations has been performed, and is demonstrating the importance of the 3.9 µm information (Mackenzie and Mecikalski 2005). For nighttime CI and new lightning initiation (LI), research is beginning to use of the µm channels over land, as well as the 3.75/3.9 11/10.8 µm channel difference (Nelson and Ellrod 1996), on GOES and MSG (which suffers from surface emissivity and visible light interference problems during daytime). For the nighttime and daytime CI, several of the 12 IR MSG channels (see Table 1) are be of high value towards enhancing the current IR indicators of convective cloud development. For guiding lightning prediction, we are challenged to use MSG to help isolate a ~1-2 km-thick 6

7 portion of a cumulus cloud that occurs below 10º C based on cloud-top microphysical indicators, e.g., cold cloud growth leading to the formation of a dbz echo through the 10º level (Williams et al. 2005). A subsequent significant goal is to incorporate the research of D. Rosenfeld (Rosenfeld et al., 2008 and the references therein) on the exploitation of the ~3.7 µm channel for the characterization of cloud droplet (effective) radii and ice microphysics, as this is correlated to in-cloud updraft strength and therefore to thermodynamic instability (e.g., Martins et al. 2007). The future of the MB06 methodology will be in the monitoring of the precipitation formation/initiation process, which ultimately equates to the monitoring of cloud microphysics. Certainly, a considerable amount of important information for nowcasting CI/LI will always come from the real-time determination of cumulus updrafts properties (e.g., via cloud-top cooling rates, updraft width, and growth a) b) c) d) Figure 3: (continued below; see caption there). 7

8 e) f) Figure 3: Comparison of nowcasting methods for case found at 1702 UTC 6 July Shown are KHTX (Hytop, AL) WSR-88D reflectivity at a 2 km height at (a) 1702 UTC and (b) 1732 UTC. Here (a) is the initial time radar reflectivity (t 0 ), and (b) is the time t=t min. The CI nowcasting method as developed in MB06 (labeled Scoring-Based Nowcasting, i.e. 7-8 of 8 fields are within range) is shown in (c). For comparison, the scoring approach in which any 4 of the 8 interest fields are within range is shown in (d), the MB06 method in which all 8 fields are within range (e), and an Optimal approach as described in the text in which only IF2, IF1, IF6 and IF4 are used together. Nowcasts (Fig. 3c, d, e and f) were created at 1702 UTC, and are valid between 1702 and 1802 UTC. See text for discussion. rates). Consequently, a close integration of the current MB06 methodology with the recent work of D. Rosenfeld will result in a CI/LI algorithm (for MSG and other geostationary satellites) that will be robust in the time-evolution monitoring of the most important physical cloud-top properties of cumuli for rainfall and lightning initiation. The main limitations to the CI methodology lie in (1) accurate cumulus cloud tracking, and (2) that 1 km-resolution data from the GOES sensor are needed (i.e. cumulus with scale <1 km are not well tracked or observed by GOES VIS and IR sensors; Mecikalski et al. 2008), which implied that the MB06 algorithm can only be used during daytime conditions. The MB06 method converts all IR data to the VIS sensor resolution. An eventual evolution towards object tracking provided 5-10 minute data from MSG (and eventually GOES-R s Advanced Baseline Imager) is planned towards mitigating these sources of error. Results for two times during the 6 July 2004 event (1702 and 1715 UTC), and at 2015 UTC on 6 July 2004, are shown in Figs. 3(a-f), respectively. Shown in (a) and (b) are current (the time when the CI nowcast was made) and future (the time when the CI nowcast becomes valid, namely minutes into the future) radar reflectivity, the MB06 scoring approach with 7-8 of 8 indicators within range [labeled, Scoring-Based Nowcast ; (c)], and the MB06 methodology in which any 4 of 8 interest fields are within their respective ranges [labeled SATCAST4 Nowcast ; (d)], the MB06 methodology in which all 8 interest fields are within their respective ranges [labeled SATCAST8 Nowcast ; (e)], and the Optimal nowcast approach as described above (f). Here, the Optimal approach is defined as using only interest fields ( IF ) 2, 1, 6 and 4 (see Fig. 1-right-hand side) to score a given GOES pixel for CI nowcasting. 8

9 From these figures, several things can be seen and understood: (1) Using the MB06 method without significant conditions leads to relatively poor skill results, as seen in Fig. 3d, with significant over predictions of CI; these over-predictions of CI have been quantified as nearly a factor of three. (2) Using the MB06 method with 7-8 of 8 mimics the Optimal results, yet the Optimal results have known skill scores per GOES pixel; (3) The Optimal approach (4 interest fields; see caption to Fig. 3) reduces the number of cumulus pixels forecasted to experience CI within the 0-1 hour timeframe by ~5-10%, compared to the MB06 method; (4) the MB06 method with 8 of 8 fields within range provides the most conservative estimate of new CI, exemplifying the ~12% FAR scores if 8 indicators are necessary for nowcasting CI. From these figures, the use of simple (unconditional scoring) suggests that the MB06 method with all 8 IR indicators in range provides the highest quality scores, and the use of conditional scoring is a way of optimizing this algorithm for either maximizing POD skills or minimizing FAR scores. 3. Benefits of the FCI (and IRS) for Satellite Nowcasting of CI and Lightning 3.1 General Enhancements for the MB06 Algorithm General improvements from the FCI, above those already provided by MSG given its existing channels and resolutions, will come via the new µm channel, the general improved infrared resolution to 2 km (above the 3 km from MSG), the routine 5-10 minute time resolution imagery, the added amount of MAMVs for tracking growing cumulus clouds, and the 1 km-resolution 10.5 µm window channel for determining cloud-top temperatures and trends. We will expand on each of these. Currently, one cause of FARs within the MB06 algorithm is due to thin cirrus overlying low-level cumulus (or simply warm low-level clouds). Spurious cloud-top cooling rates occur as these thin ice clouds (which are mostly undetectable with the current MSG Imager) travel over low-level clouds, leading to cooling rates within the 4 to 8 C ranges of the Roberts and Rutledge (2003) cooling rate radar echo change criteria. In effect, this leads to scores for cumulus pixels that are false, and subsequently can lead to these 1 km pixels being flagged as favorable for CI, when in actuality they are not. The µm channel will allow us to cloud-clear these pixels, and instead not consider them as potential CI pixels. Currently, the MB06 algorithm cannot nowcast for CI when higher clouds obscure lower level cumuli. The FCI will possess a horizontal resolution of 2 km for most channels, and 1 km for several (the 0.444, 0.96, 3.8 and 10.5 µm) channels, and even 0.5 km resolution for the 2.26 µm channel. Currently, the MB06 methodology replicates pixels in the infrared from 4 km (on GOES) and 3 km (on MSG) to 1 km. Given the 2 km and 1 km data to be expected with MTG, this pixel replication will be reduced by up to 50%, and will be alleviated entirely for the 10.5 µm channel (which is one of the most important within the MB06 method). The 5-10 minute time-resolution data from MTG will match physically better with the 1-2 km resolution infrared data, such that tracking of storms will be optimized (versus the 3 km 15 min match currently available from MSG. Lastly, the 1 km 10.5 µm channel will greatly enhance our ability to nowcast CI and LI during the day and at night. During daytime, the convective cloud mask will operate 9

10 more cleanly, and at night, the possibility exists of obtaining texture from the infrared, which is currently only available from the visible data. Certainly, we still expect to utilize MTG data (the same way we use MSG data) from the high-resolution visible (HRV), 1.6, 6.2, 7.3, 8.7, 12.0 and 13.4 µm channels to nowcast CI and LI. Data from the 0.6, 0.8 and 9.7 µm channels, and those at wavelengths >13.4 µm, are not currently anticipated to have much value in short-term thunderstorm prediction using the MB06 algorithm. 3.2 Enhanced Microphysical Monitoring As stated above, the FCI will offer several channels that will enhance our ability to nowcast CI and LI using the MB06 algorithm. All of the channels highlighted here are not available on the MSG, except for the 3.8 µm channel. Use of the new µm and 2.26 µm channel (at 1 and 0.5 km resolution, respectively), along with the resolution-enhanced 3.8 µm channel on MTG, will greatly improve the critical real-time analysis of cloud-top microphysics, along the lines of the work by Williams et al. (2005) and especially Rosenfeld et al. (2008). The µm data will help assess ice atop clouds. Once the solar reflectance and Earth emitted components are separated for the 3.8 µm channel, we may assess the cloud top microphysical characteristics with respect to drop effective radii and the size distribution of ice particles. If these sizes are known, and the relative distributions of ice, versus super-cooled water, versus water drop, are also understood, one may develop a profile of the realized stability within the convective cloud. This information can in turn be related to the abundance of the various hydrometeor types within various temperature regimes (i.e. levels) within the cloud, and relationships to the amount of lightning and rainfall production abilities of the cloud may be made. Enhancing the 3.8 µm to 1 km resolution and especially adding the and 2.26 µm spectral channel will add significant value above MSG for characterizing rainfall initiation and lightning initiation and production within convective clouds, both of which will enhance the MB06 algorithm. It is anticipated, based on evolving research plans, that a significant effort over the coming 1-3 years will be to implement the ideas of Rosenfeld et al. (2008) into the MB06 algorithm, in a manner that maximizes use of the 3.8 µm information. 3.3 Improved Atmospheric Moisture and Temperature Information The MTG FCI and the IRS will provide improved and very important information on water vapor and temperature. This will improve (at 1-2 km resolution) estimates of equivalent potential temperature (θ e ) throughout the troposphere. And even more importantly, the IRS will provide some θ e information within the atmospheric boundary layer. Figure 4 presents the weighting functions for temperature (Fig. 4a) and moisture (Fig. 4b) that are expected from the IRS, which exemplify how 1 km, and 1 K (in temperature) and 15% (in moisture) resolution soundings will be retrieved from the hyperspectral data. The IRS will have cm -1 resolution spectral data. This added 10

11 accuracy in θ e will provide significant information for nowcasting CI and lightning/li. For CI, it is well known that increasing low-level, boundary layer moisture is one main process for decreasing atmospheric stability, and weakening/breaking a capping inversion. Clearly then, any satellite-based way of estimating θ e at a high-spatial resolution will lead to improvements in CI forecasts. For LI, the shape of the stability profile provides key information for diagnosing whether lightning will occur, and even for estimating how much lightning (in terms of flash rates, and source counts) will accompany a convective storm (MacGorman and Rust 1998). Figure 4: Weighting functions for temperature (Fig. 4a) and moisture (Fig. 4b) that are expected from the IRS, which exemplify how 1 km, and 1 K (in temperature) and 15% (in moisture) resolution soundings will be retrieved from the hyperspectral data. Figures courtesy of Dr. J. Li (U. Wisconsin, CIMSS) for research on future hyperspectral sounding capabilities. Lastly for moisture, the new 1 km-resolution 0.96 µm channel on the FCI will provide high-quality precipitable water (PW) information at 1 km spatial resolution. These data will also be useful towards assessing rainfall potential (whether there will be lighter or heavier precipitation), and also to assess how the atmospheric stability is being altered by surface fluxes of moisture, and/or moisture advection. 3.4 Aerosol Information for Rainfall Initiation The 1 km resolution µm channel, used together with 2.26 µm channel, on MTG will provide information on boundary layer aerosols. Data on aerosol size distributions (and even type; marine salt versus fine dust) plays a role in the rainfall generation/ initiation process. MTG should be able to provide to the MB06 algorithm information as to the ease that CI occurs, especially across various convective environments. 4. Analysis of MSG during COPS for Infrared CI Interest Field Definitions The need to enhance the current GOES satellite-centric MB06 algorithm for use with additional IR channel information, as provided currently by MSG and especially by the FCI and IRS on MTG, is substantial. As a means of performing this important 11

12 enhancement, data from the COPS field experiment were used to define the range of values (and a critical value) for several additional IR indicators MSG provides about cloud-top conditions during the CI process. Data used for this component of the study included MSG IR data and radar reflectivity. (a) (b) (c) (d) Figure 5: Sequence of 4 images, (a)-(d), separated roughly by 20 minutes each, showing the development of cumulus clouds in the congestus stage (a; upper left) to the glaciation/cumulonimbus stage (c; lower left), and finally to the thunderstorm stage (d; lower right). See for the source of these images, by R. Hankers. The precise plan was to estimate the changing IR properties for quasi-stationary convection (as it initiated into ~35 dbz echo-producing storms); data chosen therefore, was over the Baden Württember region, along the Rhine River Valley, where CI was closely coupled to topographic features. This was done as a means of circumventing the need for producing MAMVs to track cumulus in the pre-ci stages, which is a computational burden. Fortunately, enough data exists for quasi-stationary (i.e. storms that moved only 3-6 km, or 2 MSG pixels) storms that a robust data set was obtained. Several times per day when CI was actively occurring were considered in this analysis, and extended from 1100 to 1730 UTC. Figure 5(a-d) exemplifies the actual cumulus cloud behavior that we are interested in monitoring IR properties for, as a group of cumulus clouds grow from a congestus state to a cumulonimbus. It is these clouds, following this sequence of events (being in this case, nearly quasi-stationary) that the MB06 algorithm monitors. For this component of the project, numerous clouds of this kind were used to develop the statistics in Table 2. 12

13 Table 2 describes the CI interest fields that will be under evaluation when using MSG data. The preliminary work done for this project was to evaluate the critical values per interest field, and to assess how some of the new MTG channels (as currently available on MSG) will be used within the MB06 methodology. Here, many of the 12 MSG channels were considered. Table 2: MSG satellite related IR interest field that were preliminarily evaluated for use within the MB06 algorithm. A total of 19 possible indicators were considered. See text for description of each indicator. MSG CI Interest Field Critical Value Physical Interpretation & Comments [1] 10.8 µm T B [IF1] < 0 C Cloud-top coldness [2] 10.8 µm T B Time Trend [IF2, IF3] < 4 C/15 mins Cloud growth rates T B /30 min < T B /15 min [1] 10.8 µm T B drop to <0 C [IF4] Within prior 30 mins Cloud-top glaciation [1] µm difference [IF5] 30 C to 10 C Cloud growth into dry air aloft [1] µm Time Trend [IF6] > 2-3 C/15 mins Cloud growth rates into dry air aloft [2] µm difference [IF7] µm difference 25 C to 5 C 0 to 3 C Cloud growth information, into mid- and upper troposphere (redundant) [2] µm Trend [IF8] µm Trend > 3 C/15 mins > 1 C/15 mins Cloud growth rate information, into mid- and upper- troposphere (redundant) [1] µm difference [IF9] Transition across 0 C in min Cloud-top glaciation (see also Rosenfeld et al. 2008) [1] µm Time Trend [IF10] > -5 C Cloud-top glaciation (see also Rosenfeld et al. 2008) [1] µm difference [IF11] 40 C to 15 C Cloud growth into dry air aloft (may be redundant with µm) [1] µm Time Trend [IF12] > 3-4 C/15 mins Cloud growth rates into dry air aloft (may be redundant with µm) [1] µm difference [IF13] Look-up table for microphysics and glaciation (0-1) Cloud-top glaciation (daytime only) [1] µm Time Trend [IF14] Positive trend towards +1 Cloud-top glaciation rates (daytime only) [1] µm difference [IF15] µm difference Look-up table for microphysics; < 0 C (use existing product) Cloud-top glaciation towards precipitation formation [1] µm Time Trend [IF16] -2-5 C/15 min Cloud-top glaciation rates towards precipitation formation [1] µm difference [IF17] Positive difference Assessing if cumulus broke capping inversion [19] CI Indicators Table 2 shows that from MSG, 19 IR-related fields are available (keeping in mind that additional interest fields will be developed through the use of MTG, yet we cannot completely say with certainty what the critical values and/or methods needed to process them within MB06 given the data do not yet exist). In several cases, redundancy exists and therefore we do not anticipate needing all 19 fields. All of the critical values were established from observing MSG data during the CI process, as noted above, yet are likely subject to some future modifications as (a) the MB06 algorithm is tuned to various convective-regime environments, and (b) the MB06 algorithm is optimized for skill scores, whether for high POD, or low FAR (or both in the case of the Threat Score). 13

14 In this table, the first 10 fields are essentially those used for the GOES processing, and therefore the fields that exploit the 12.0 and 13.4 µm channels are likely very much redundant. Both fields are used to assess cloud growth into dry middle and upper tropospheric levels. For all following CI interest fields, the main benefit for their use within the MB06 method is their ability to assess changes in cloud-top microphysics, namely the transition from water drops, to super-cooled drops, and eventually to ice crystals. This is key in the production of rainfall at the ground (except in warm topped convection in tropical environments, as mentioned above). The exceptions are the µm channel difference, and the µm difference. The use of the 3.8 µm channel via the µm channel difference, and the trend of this difference, will be very valuable in observing this transition toward cloud-top glaciation. During the daytime, the visible reflected and infrared emitted components need to be separated in order for proper interpretation. The µm difference (and its trend) can also be used to assess ice versus water microphysical structures, yet it can only be used only during the day since the 1.6 µm channel measures reflectivity only (versus thermal emission). The use of the µm is critical for assessing cloud-top glaciation, and is not limited to use only during the day or only during the night since the 8.7 µm channel measures purely infrared thermal information. In the end, the optimal use of the microphysical information may come from look-up tables (developed from diagnostic studies), or via sophisticated methods such as that of Rosenfeld et al. (2008) via the analysis took of Lensky and Rosenfeld (2008). For LI, this microphysical information, gleaned from the 1.6, 3.8 and 8.7 µm channels, will prove most useful. The ongoing work of Siewert (2008; see referenced therein) is optimizing the MB06 algorithm for LI nowcasting. Use of GOES data alone is quite limited, especially during daytime. In contrast, with the additional channels on MSG, it is clear that these three spectral channels will substantially improve our ability to time-monitor the occurrence of significant ice (microphysics) and even ice fluxes (e.g., grauple) above certain threshold temperatures, as important for charge generation (see MacGorman and Rust 1998). Prior to the exhaustive studies anticipated to occur at EUMETSAT in later 2008 and 2009, the only alterations we can make at this time to Table 2 for LI nowcasting are: (a) considering a longer lead times on cloud-top cooling rates (namely min) as a means of nowcasting LI within ~90 min., (b) giving increasing weight to larger cloud-top cooling rates, as a means of inferring the flux of frozen hydrometeors across the 10 to 15º C isotherm, (c) heavily weighing the time trend of the µm channel difference, which is not used in the MB06 methodology today. All of these results are from Siewert (2008), and will be developed significantly further in late 2008 through 2011 via two new research projects (see Section 5 below). Use of the µm channel (plus its difference) will be used in a similar manner to the µm channel, except that it is anticipated to provide information (when used in conjunction with the µm difference) on cumulus cloud growth and deepening rates. It therefore may provide redundant information to the 10.8 µm cloud-top cooling rates as currently used by the MB06 algorithm. Lastly, the µm channel difference is tentatively assumed to provide information on mid-level moisture and/or inversion information that subsequently can help determine if CI is likely. In this way, this CI indicator may provide less information on cloud-top microphysics or growth, but 14

15 instead on thermodynamic inhibitors to CI through the assessment of stability conditions just above cloud top (before the cumulus break a capping inversion onwards to CI). 5. Outlook This study was performed to evaluate the expected improvements to satellite-based CI and LI nowcasting, over the 0-90 min timeframe from present as afforded by MTG s FCI and IRS. Use of MSG data, as collected during the 2007 COPS field campaign, provides us a step-function view of how MTG data will improve CI. This was accomplished by defining a number of new interest fields from MSG, and by assessing the critical values of these fields for cumulus clouds undergoing CI during COPS. The definitions of ~11 new interest fields were developed. It is likely that the full benefits of MTG will not be fully known until this instrument suite (the FCI coupled with the IRS) becomes fully operational, by ~2015. It is anticipated that a more rigorous study will eventually refine the values of each new MSG interest field, as listed in Table 1, for both CI and LI. Fortunately, new work via EUMETSAT will begin in late 2008 (complete with a visiting scientist from UAH to EUMETSAT in Darmstadt, Germany). The plan will be to fully evaluate: (1) The use of MSG fields (as listed in Table 1) for both CI and LI nowcasting; (1) The accuracy of MSG for CI and LI nowcasting, which will consist of developing a dataset of co-located satellite, radar and lightning fields for the nowcasted MSG pixels (requiring time-translation and correlation- based mapping of current imagery to future radar and lightning locations); (1) And, a new National Aeronautics and Space Administration (NASA) funded effort for UAH will use data from the MODerate resolution Infrared Spectrometer (MODIS) and CloudSat s Cloud Profiling Radar (along with GOES and MB06 output fields) to optimize the MB06 algorithm for nowcasing CI and LI over various convective regimes. Presently, it is understood that this method does not perform optimally in Tropical and especially marine environments, when ice microphysics may be less important in the CI process. Funding for Dr. Mecikalski and his team at UAH also comes via the GOES-R Risk Reduction programme in the United States. The GOES-R satellite is slated to become operational in ~2014, and will be similar to MSG in its spectral and imaging capabilities. This funding is already providing the motivation to transition the MB06 algorithm into the operational communities nowcasting tools, especially those related to aviation weather forecasting. 15

16 References Ackerman, S. A., 1996: Global satellite observations of negative brightness temperature differences between 11 and 6.7 µm. J. Atmos. Sci., 53, Bedka, K. M., and J. R. Mecikalski, 2005: Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows. J. Appl. Meteor. 44, Bedka, K. M., C. S. Velden, R. A. Petersen, W. F. Feltz, and J. R. Mecikalski, 2007: Statistical comparisons between satellite-derived atmospheric motion vectors, rawinsondes, and NOAA Wind Profiler observations. Accepted. J. Appl. Meteor. Berendes, T. A., and J. R. Mecikalski, K. M. Bedka, and U. S. Nair, 2006: Convective cloud detection in satellite imagery using standard deviation limited adaptive clustering. In preparation. J. Appl. Meteor. Kessler, E., 1987: Kinematic effect of vertical drafts on precipitation near Earth s surface. Mon. Wea. Rev. 115, Lensky, I. M., and D. Rosenfeld, 2008: Cloud-aerosols-precipitation satellite analysis tool (CAPSAT). Atmos. Chem. Phys. Discuss., 8, MacGorman, D. R., and W. D. Rust, 1998: The Electrical Nature of Storms, Oxford University Press, Inc., 419 pp. Mackenzie, W., Jr., and J. R. Mecikalski, 2005: Using Multi-Spectral Satellite Remote Sensing Techniques to Nowcast Nocturnal Convection Initiation. In 21st Conf. on Weather Analysis and Forecasting/17th Conf. on Numerical Weather Prediction, 1-5 August 2005, Washington, D.C. Mecikalski, J. R., K. M. Bedka, S. J. Paech, and L. A. Litten, 2008: A statistical evaluation of GOES cloud-top properties for nowcasting convective initiation. Mon. Wea. Rev., Accepted. Mecikalski, J. R., K. M. Bedka, and S. J. Paech, 2006: Recent developments in convective initiation forecasting using GOES. In 14th Conf. on Satellite Meteorology and Oceanography, 29 Jan 2 Feb 2006, Atlanta, GA. Nair, U. S., R. C. Weger, K. S. Kuo, and R. M. Welch, 1998: Clustering, randomness, and regularity in cloud fields 5. The nature of regular cumulus cloud fields. J. Geophys. Res., 103, Nelsen, J. P., and G. P. Ellrod, 1996: Improved GOES-8 multispectral (10.7 µm 3.9 µm) satellite imagery to detect stratus and fog at night. Preprints, 8th Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Raymond, D. J., 1978: Instability of the low-level jet and severe storm formation. J. Atmos. Sci., 35, Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm Initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, Rosenfeld D., W. L. Woodley, A. Lerner, G. Kelman, D. T. Lindsey, 2008: Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase. In press, J. Geophys. Res. Schmetz, J, S. A. Tjemkes, M. Gube, and L. van de Berg, 1997: Monitoring deep convection and convective overshooting with METEOSAT. Adv. Space Res., 19,

17 Siewert, C., 2008: Nowcasting lightning initiation through the use of infrared observations from the GOES satellite. Masters Thesis, University of Alabama in Hunstville. In progress. Williams, E., V. Mushtak, D. Rosenfeld, S. Goodman, and D. Boccippio, 2005: Thermodynamic conditions favorable to superlative thunderstorm updraft, mixed phase microphysics and lightning flash rate. Atmos Res., 76, List of Figures 1. Tendencies and values 8 CI interest fields (bracketed numbers) used for GOES and MODIS data within the Mecikalski and Bedka (2006) algorithm. [Page 4] 2. GOES-12 convective initiation nowcasting over a Storm Prediction Center severe storm risk region. [Page 5] 3. Comparison of nowcasting methods for case found at 1702 UTC 6 July [Page 7-8] 4. Weighting functions for temperature (Fig. 4a) and moisture (Fig. 4b) that are expected from the IRS [Page 11] 5. Sequence of 4 images showing the gradual development of cumulus clouds toward convective initiation. [Page 12] List of Tables 1. Summary of visible and infrared channels data that the MSG SERVIRI and MTG FCI and IRS instruments can provide for CI and LI nowcasting. [Page 6] 2. MSG satellite related IR interest field that were preliminarily evaluated for use within the MB06 algorithm. [Page 13] 17