Intraseasonal soil moisture variability has the potential to feed back onto the West Africa monsoon circulation through its influence on surface turbulent fluxes and planetary boundary‐layer characteristics. Using satellite observations and an atmospheric reanalysis, we investigate intraseasonal soil moisture–atmosphere feedbacks triggered by large‐scale dynamics within the West African monsoon. Surprisingly, even though the surface response across the Sahel to strong convection is short‐lived (days) and precipitation accumulations are spatially and temporally heterogeneous, a coherent regional‐scale surface response to intraseasonal variability is observed. This surface response then feeds back onto the West African monsoon circulation. For example, during a dry intraseasonal event, Sahelian surface soil moisture significantly decreases, which elevates surface temperatures by 1.5 °C and shifts the monsoon circulation southward by approximately 1.5° latitude. Also, during a wet event the surface moistens and cools which leads to a northward monsoon shift. Alongside a low‐level wind response, the African Easterly Jet (AEJ) also responds to surface changes due to variations in the meridional temperature gradient. For example, an increased temperature gradient during a dry event intensifies and shifts the AEJ southward. The combined response of low‐level monsoon westerlies and the AEJ impacts low‐level shear and characteristics of strong convection. Elevated low‐level shear during a dry event promotes an intensification of deep convection across southern West Africa. This study provides new insight into the sensitivity of the West African monsoon circulation to intraseasonal soil moisture feedbacks and encourages similar research in other regions. An improved understanding and model representation of soil moisture–atmosphere feedbacks has the potential to improve forecasts beyond daily time‐scales and enhance early warning systems.
The intensity of cosmic ray neutrons is inversely correlated with the amount of water present in the surrounding environment. This effect is already employed by around 50 neutron sensors in the COSMOS-UK network to provide daily estimates of soil moisture across the UK. Here, these same sensors are used to automatically provide estimates of snow water equivalent (SWE). Lying snow is typically ephemeral and of shallow depth for most parts of the UK. Moreover, soil moisture is usually high and variable, which acts to increase uncertainties in the SWE estimate. Nevertheless, even under such challenging conditions, both above ground and buried cosmic ray neutron sensors are still able to produce potentially useful SWE estimates. Triple collocation analysis suggests typical uncertainties of less than around 4 mm under UK snow conditions.
In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly-evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 hours. Using Land Surface Temperature (LST) as a proxy for soil moisture, we characterise the state of the surface energy balance in NRT. We identify the most convectively-active parts of Mesoscale Convective Systems (MCSs) from spatial filtering of cloud-top temperature imagery. We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For a Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6-18 hours, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics.
<p>Across West Africa rain-fed agriculture fulfils approximately 80% of the food needs of the population and employs 60% of the workforce. It is therefore critical to understand the effects of intraseasonal rainfall variability across West Africa. Previous work has shown that land-atmosphere interactions across West Africa can influence daily variability in deep convection characteristics and the impact of 10-25 day precipitation variability. Using earth observations and reanalyses, this study investigates the land surface response to 20-200 day precipitation variability and its impact on land-atmosphere interactions and the West African monsoon.</p><p>&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; Surprisingly, even though the sensitivity of the land surface across the Sahel to strong convection is short-lived (days) and daily precipitation patterns are strongly heterogeneous, a coherent regional-scale land surface response to 20-200 day precipitation variability is observed. This sensitivity of the land surface affects land-atmosphere interactions on a regional scale and perturbs the West African monsoon circulation. For example, during sub-seasonal periods of low rainfall, soil moisture significantly decreases across the Sahel and land surface temperatures increase by up to 2&#176;C. Surface drying and warming across the Sahel is associated with an intensified heat low and a northward shift of low-level monsoon westerlies. During periods of high rainfall, the surface moistens and cools, which is associated with a high pressure tendency across the Sahel. This high pressure tendency dampens the heat low circulation across West Africa and reduces regional moisture fluxes. We show that the land surface response to 20-200 day rainfall variability across West Africa can have a significant impact on the monsoon circulation. This suggests that improving the representation of land-surface processes across West Africa has the potential to improve sub-seasonal forecast predictability and enhance early warning systems.</p>
<p>In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly-evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 hours. Using Land Surface Temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively-active parts of Mesoscale Convective Systems (MCSs) from spatial filtering of cloud-top temperature imagery.</p><p>We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For the Science for Weather Information and Forecasting Techniques (SWIFT) Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6-18 hours, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics.</p>
<p>Forecasting the potential for flood-producing precipitation and any subsequent flooding is a challenging task; the process is highly non-linear and inherently uncertain. Acknowledging and accounting for the uncertainty in precipitation and flood forecasts has become increasingly important with the move to risk-based warning and guidance services which combine the likelihood of flooding with the potential impact on society and the environment.</p><p>A standard approach to accounting for uncertainty is to generate ensemble forecasts. Here the national Grid-to-Grid (G2G) model is coupled to a Best Medium Range (BMR) ensemble which consists of three models spanning different time horizons: an ensemble nowcast for the first 6h, which is blended with the short-range 2.2 km Met Office Global Regional Ensemble Prediction System (MOGREPS-UK) ensemble up to 36h and the ~20 km global MOGREPS-G up to day 6. The G2G model is driven by 15-minute accumulations on a 1 km grid.</p><p>16-months of precipitation and river flow ensemble forecasts have been processed to develop and assess a joint verification framework which can facilitate the evaluation of the end-to-end forecasting chain. Analysis concluded the following: (1) daily precipitation accumulations provide the best guidance in terms of rain volume for hydrological impacts. One reason may be because it removes the impact of timing errors at the sub-daily scale. However, sub-daily precipitation can be more closely related to river flow on an ensemble member-by-member basis. (2) Observation uncertainty is important. The same forecasts verified against three different observed precipitation sources (raingauge, radar or merged) can provide markedly different results and interpretations. G2G river flow performance can also be affected, when driven by these datasets rather than forecasts. (3) The change in precipitation-intensity with model is evident and has an impact on downstream modelling and verification. (4) The period used for ensemble verification should be at least two years. The 16-month test period was sufficient for generating enough precipitation threshold-exceedances for the 95th percentile: but insufficient for higher thresholds and for river flow thresholds above half the median annual maximum flood at sub-regional scales. (5) A new method of presenting Time-Window Probabilities (TWPs) has been developed for precipitation thresholds that are hydrologically relevant. Verification of these shows that probabilities are larger, and more reliable so that users can have greater confidence in them. (6) Overall precipitation forecast skill was far more uniform than for river-flow, primarily because the atmosphere is a continuum whilst catchments are finite and subject to external, non-atmospheric factors including antecedent moisture. (7) Though G2G can be sensitive to precipitation outliers, the precipitation ensemble is generally under-spread and spread does not appear to amplify or propagate to enhance the river flow ensemble spread, so spread is reduced rather than increased in the downstream application.</p>
<p>Flash flooding from intense rainfall frequently results in major damage and loss of life across Africa. Over the Sahel, intense rainfall from Mesoscale Convective Systems (MCSs) is the main driver of flash floods, with recent research showing that these have tripled in frequency over the last 35 years. This climate-change signal, combined with rapid urban expansion in the region, suggests that the socio-economic impacts of flash flooding will become more frequent and severe. Appropriate disaster preparedness, response, and resilience measures are required to manage this increasing risk.</p><p>The NFLICS (Nowcasting FLood Impacts of Convective storms in the Sahel) project has co-developed a prototype early warning system for Senegal, incorporating nowcasting of heavy rainfall likelihood and flood risk from MCSs at city and sub-national scales. This system uses remote sensed satellite data and has been developed in partnership with the national meteorological agency (ANACIM) to operate quickly in real-time. To identify convective activity, wavelet analysis is applied to Meteosat data on cloud-top temperature for historical periods (2004 to 2019) and for the start-time of a nowcast. Data on historical convective activity, conditioned on the present location and timing of observed convection, are used to produce probabilistic forecasts of convective activity out to six hours ahead. Verification against the convective activity analysis and the 24-hour raingauge accumulations over Dakar suggests that these probabilistic nowcasts provide useful information on the occurrence of convective activity. The highest skill (compared to nowcasts based solely on climatology) is obtained when the probability of convection is estimated over spatial scales between 100 and 200km, depending on the forecast lead-time considered. Furthermore, recent advances have included incorporation of land surface temperature anomalies to modify nowcast probabilities &#8211; this recognises that MCS evolution favour drier land.</p><p>A flood knowledge database, compiled with local partners, allows estimation of the flood risk over Dakar given the identified probability of convective activity. The flood hazard is estimated from the probabilistic convective-activity nowcast when combined with information on the historical relationship between convective activity and precipitation totals. Information on the antecedent conditions can also be included, with a higher level of hazard associated with recent rainfall and already-wet conditions. Flood vulnerability is estimated at the local scale from post-event analysis of the 2009 flood events along with information from recent modelling studies and flood-alleviation measures. The combined information from nowcasts of convective-activity and flood-risk is visualised through an interactive desktop GUI and an online portal. Operational trials over the 2020 and 2021 rainy seasons, and during intensive nowcasting testbeds with researchers and forecasters, has shown the utility of these new nowcast products to support Impact-based Forecasting.</p>
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