Convective activity and land surface temperature patterns (latest daily summary)

To provide a summary of deep convective activity (starting from 10UTC each morning) and its relationship with the land surface state.

There are two datasets plotted in this product. The shaded background depicts daytime Land Surface Temperature (LST) patterns. These provide a high spatial resolution near-real time proxy for soil moisture structures. In the Sahel, areas which have experienced rain in the previous few days (and therefore have relatively wet top soil) can maintain high rates of evapotranspiration, accompanied by low sensible heat flux and low LSTs. Every 15-minutes, the LandSAF produce an LST image from Meteosat using infra-red channels 9 and 10. For each 15-minute image, we apply additional cloud-screening and compare with a climatology based on data for the same pixel and month from the period 2004-2015. We then compute a daytime mean LST Anomaly (LSTA) using all cloud-screened data between the hours of 0700 and the current time or 1700 UTC, whichever is the earlier. Over the first few hours of the morning, the LSTA patterns emerge as more cloud-free data become available. From 10UTC, the LSTA pattern is sufficiently robust to start a new day’s animation of convective activity. Where there is no information about LSTA, the pixel is shown in grey. During the Sahel wet season, such conditions predominate in Southern West Africa. Overplotted on daytime mean LSTA is information on deep convection, based on channel 10 cloud-top temperatures. The contours indicate the presence of “convective cores” (Klein et al, 2018) at different times. These areas are colder than the surrounding cloud field and are produced from a spatial filtering process which distinguishes the most convectively active parts of a cold cloud system (the cores) from slightly warmer regions (for example the stratiform cloud shield). The core areas are responsible for the vast majority of intense rain rates in the Sahel. Because the cores are based only on infra-red information, there is no degradation of their information content at night. The spatial filtering applied identifies cold cloud (<-50C) features on spatial scales of 25-50km. Due to the maximum scale at which cores are identified, relatively rare cases of extremely large areas of very cold cloud (>100 km) will show up as having cores around their edge but not at the centre. In such cases, intense rain rates should still be assumed to be centred on the coldest cloud area, and associated with a very intense convective region considerably larger than 50km across.

This product provides a quick summary of where intense convection occurred during the period of interest relative to the underlying surface. It is therefore useful for evaluating the impact of the land surface on the occurrence of heavy rainfall. The development of cores within mature convective systems is modulated by soil moisture, particularly during the afternoon and evening, with cores favoured over drier surfaces. If the land surface has had a strong impact on the pattern of severe convection across the region in the period, the contours depicting cores will fall predominantly on redder (warmer, drier) areas

Land surface state on the likelihood of convection

To quantify the potential modulation of today’s land surface state on the likelihood of intense convective rainfall based on statistically robust climatological relationships.

There are two datasets plotted in this animation. The shaded background remains fixed throughout the animation and quantifies the climatological modifying effect of land surface state on the likelihood of convection. It is based on Land Surface Temperature (LST) patterns observed by Meteosat. These provide a high spatial resolution near-real time proxy for soil moisture structures. In the Sahel, areas which have experienced rain in the previous few days (and therefore have relatively wet top soil) can maintain high rates of evapotranspiration, accompanied by low sensible heat flux and low LSTs. Every 15 minutes, the LandSAF produce an LST image from Meteosat using infra-red channels 9 and 10. For each 15-minute image, we apply additional cloud-screening and compare with a climatology based on data for the same pixel and month from the period 2004-2015. We then compute a daytime mean LST Anomaly (LSTA) using all cloud-screened data between the hours of 0700 and the current time or 1700 UTC, whichever is the earlier. Over the first few hours of the morning, the LSTA patterns emerge as more cloud-free data become available. In this product, we translate daily LSTA to a land modification factor by considering the climatological relationship between LSTA and convective cores at the given validity time of day. These statistics come from analysis of where convective cores occurred during the years 2004-2015 relative to LSTA values. The statistics are computed by month (June to September only) and within 3 latitude bounds (south of 12.5N, 12.5-15N and north of 15N). We determine for each pixel where today’s LSTA value sits within the climatology for the month and latitude band in question and read off the associated probability of a convective core from the historical data given the strength of that LSTA. We use the LSTA value 1 degree to the east of the target pixel as the statistics show that long-lived convective systems are most sensitive to the land surface upstream of the target. The land modification factor plotted represents the percentage increase (or decrease) in likelihood of a convective core given the land surface state. It takes no account of the current state of the atmosphere, and depends only on upstream land conditions. From 10UTC, the LSTA pattern is sufficiently robust to start producing a new set of land modification factors for the day. Overplotted on the land modification factor map is information on deep convection, based on channel 10 cloud-top temperatures in the most recent period. A black contour line marks out the extent of cloud-tops colder than -60°C and shading indicates the presence of a “convective core” (Klein et al, 2018). These areas are colder than the surrounding cloud field and are produced from a spatial filtering process which distinguishes the most convectively active parts of a cold cloud system (the cores) from slightly warmer regions (for example the stratiform cloud shield). The core areas are responsible for the vast majority of intense rain rates in the Sahel. Because the cores are based only on infra-red information, there is no degradation of their information content at night. The spatial filtering applied identifies cold cloud (<-50C) features on spatial scales of 25-50km. Due to the maximum scale at which cores are identified, relatively rare cases of extremely large areas of very cold cloud (>100 km) will show up as having cores around their edge but not at the centre. In such cases, intense rain rates should still be assumed to be centred on the coldest cloud area, and associated with a very intense convective region considerably larger than 50km across.

These images provide a quantification of how the land surface affects the likelihood of intense convection in the coming hours. Where convection is active, a nowcaster can look upstream (to the west) to see if there are regions which are more (red) or less (blue) likely to experience convection in the upcoming hours. The predictability implied by this method comes from the role that soil moisture plays in creating spatial variability in the atmosphere. The modulation factors are strongest during the afternoon and early evening but are still robust (though weak) at 03 UTC. The predictive skill also varies by month and latitude, driven primarily by the presence of vegetation cover; where vegetation is sparse, the relationship between LSTA and surface fluxes is strongest. Cloud cover restricts the use of this technique over Southern West Africa during the June-September period.

Probability Nowcast of Convective Structures

To provide rapidly updated probability nowcasts of convective structures occurring over the next 6 hours during the summer monsoon (July-September) in West Africa. These convective structures are associated with heavy rainfall events so useful for short-term forecasting and warning.

Nowcasts are produced every 15 minutes, out to 6 hours and have an hourly timestep. Note, nowcasts are only produced if convective structures are present in the latest IR10.8 Meteosat Second Generation (MSG) cloud top temperature image. The latest nowcast animation and 2 hour and 6 hour lead-time plots are shown on this page along with the probability time-series graphs for six locations of interest.

Past nowcasts are available here

The nowcast plots have the forecast origin and lead-time in hours in the top right. The probabilities (0-100%) represent the chance of a convective structure occurring within a given spatial scale of the coloured pixel. The given spatial scale increases with lead-time, as shown by the black box in the top right of the plot. This distance has been optimised using past data and attempts to balance having useful forecast skill against the increased spatial uncertainties in storm locations at longer lead-times.

Method:

Version 1 of the NFLICS probability nowcasts of convective structures only require IR10.8 MSG Cloud Top Temperature. Convective structures are identified from the IR10.8 image using a wavelet-transform method based on (Klein et al. (2018); Klein & Taylor (2020)). A “conditional climatology” approach is used to produce the nowcasts. The nowcasts use the convective structures identified at the start of a nowcast to “look up” the likely future locations of convective structures based on a historical analysis over the period JJAS 2004-2019. The conditional climatology (i.e. what has happened in the next 6 hours given there is a convective structure at this location) is calculated for each time of day and across a grid of source area locations. As a result, the NFLICS nowcasts can potentially allow for common decay and growth sequencies. The outputs are probabilities of convective structures occurring rather than ensembles of possible future storms. The NFLICS products do not explicitly infer or advect recent storm trajectories.

Version 2 (used in the Testbed displays), uses recent Land Surface Temperature anomalies to modify the Version 1 nowcast probabilities. The probability adjustments are based on historical analysis that shows cool/wet areas are less favourable for convection and heavy rain, whilst warm/dry areas are more favourable.

The NFLICS probability nowcasts of convective structures have been co-developed with ANACIM and aimed to support real-time short-term forecasting and warning of heavy rainfall and flash flooding. Additional Impact-based Forecasting displays have been developed for the capital Dakar. The nowcasts are quickly available, within a minute of receiving a IR10.8 cloud top temperature cut-out for the NFLICS West Africa domain. Whilst initially developed with ANACIM for Senegal, the domain has immediate use for the surrounding countries and potential for the method to be extended to other domains and rainfall regimes.

Latest nowcast animation

2 hour nowcast plot

6 hour nowcast plot

Probability time-series graphs