Discriminating raining from non-raining clouds at mid-latitudes using Meteosat Second Generation daytime data

International audienceA new method for the delineation of precipitation during daytime using multispectral satellite data is proposed. The approach is not only applicable to the detection of mainly convective precipitation by means of the commonly used relation between infrared cloud top temperature and rainfall probability but enables also the detection of stratiform precipitation (e.g. in connection with mid-latitude frontal systems). The presented scheme is based on the conceptual model that precipitating clouds are characterized by a combination of particles large enough to fall, an adequate vertical extension (both represented by the cloud water path (cwp)), and the existence of ice particles in the upper part of the cloud. The technique considers the VIS0.6 and the NIR1.6 channel to gain information about the cloud water path. Additionally, the channel differences ?T8.7-10.8 and ?T10.8-12.1 are considered to supply information about the cloud phase. Rain area delineation is realized by using a minimum threshold of the rainfall confidence. To obtain a statistical transfer function between the rainfall confidence and the channel differences, the value combination of the four variables is compared to ground based radar data. The retrieval is validated against independent radar data not used for deriving the transfer function and shows an encouraging performance as well as clear improvements compared to existing optical retrieval techniques using only IR thresholds for cloud top temperature

Analysis and integration of regional scale temperature datasets into a seasonal crop monitoring system.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Populations in excess of 20 million people in southern Africa annually face food insecurity. This number increases appreciably when detrimental seasonal climate conditions lead to widespread reductions in crop harvests. This situation has led to the development of regionalscale crop monitoring systems that incorporate crop-specific water balance (CSWB) models for early detection and warning of impending weather-related crop production shortfalls. Early warning of anticipated reductions in crop harvests facilitates early action in responding to potential crises. One such system, used by the Famine Early Warning Systems Network (FEWS NET) in southern Africa for crop monitoring, calculates the water requirements satisfaction index (WRSI) using a CSWB model. Operationally, CSWB models for calculating WRSI have used a static length of growing period (LGP) to bracket the period over which rainfall and evapotranspiration variations can affect crop yields. In the long term, concerns have been raised by some studies on the impact of rising air temperatures on crop production. There is therefore a need to incorporate the impacts of air temperature on crops directly into food security monitoring systems, in order to improve the accuracy of these monitoring systems in identifying and locating weather-related crop production shortfalls. This study sought to assess the potential improvements that can be introduced to the crop monitoring system in general and the WRSI in particular, by incorporating air temperature data into the CSWB model. To address this objective, daily maximum and minimum air temperature grids derived from a general circulation model reanalysis were used to generate thermal time estimates, expressed as growing degree day (GDD) grids for a maize crop. The GDDs were used to estimate the LGP of maize for each pixel of each summer season (which typically runs between around October and March) from 1982/1983 to 2016/2017 in southern Africa. The variable, temperature-driven LGP estimates compared favourably with LGP values obtained from literature for a few sample locations. The variable LGP was used to calculate the WRSI for 35 seasons, and the resultant WRSI showed improved correlation with historical yield estimates compared to the static-LGP WRSI, particularly after the farming practice of planting on multiple dates was taken into consideration. Various expressions of WRSI were considered in the analysis, including WRSI calculated assuming planting at the onset of rains, WRSI aggregated from varying number of separate planting dates, including three and six planting dates as test examples, and WRSI calculated using a modified soil water holding capacity to better capture local soil management practices. Historical maize yields for sub-national administrative units from seven southern African countries were correlated with the various WRSI expressions. Gridded GDD data that reflect the accumulated severity of extreme warm temperatures experienced during the crop growth period, referred to as extreme growing degree days, or eGDD, were also noted to have significant correlations with historical maize yields in several southern African countries. A number of variants of the eGDDs were tested, including eGDDs accumulated throughout the crop’s growth period, eGDDs that only occurred simultaneously with periods of crop water deficit, eGDDs that occurred during the crop flowering stage, and eGDDs scaled by the severity of crop water deficit. In several areas, the various eGDD expressions indicated higher correlations with yield than any of the WRSI variants indicated. The eGDD parameter showed strong correlations with WRSI, suggesting that the accumulated high temperatures were a reflection of the influence of low rainfall and low soil moisture during episodes of high temperature. More work is required to calibrate and refine the temperature-based monitoring parameters that were developed in this study, at local, sub-national scales. In particular, assumptions of the linearity of maize yield response for the various parameters should be tested. Potential improvements of the combined eGDD-water deficit parameter through the incorporation of prediction coefficients and constants should also be tested. A secondary aim of the study was to explore how readily available temperature-related datasets can be utilized to derive air-temperature metrics. To this end, satellite-derived thermal infrared (TIR) brightness temperature data were analysed, and a method was developed for identifying cloud cover, while simultaneously estimating cloud-free diurnal brightness temperature curves, using a single TIR satellite channel. The diurnal brightness temperature curves were developed using a sinusoidal and exponential model for daytime and nighttime respectively, utilizing modifications that enabled the curves to be estimated from two known temperatures at any two given times with cloud-free brightness temperature scenes. Comparison of the cloud mask developed in this study with an existing operational cloud mask based on a methodology developed by the EUMETSAT Satellite Applications Facility for Nowcasting gave an accuracy of 85.4%, when the operational method was considered as truth in a confusion matrix analysis. Situations were identified in which the different cloud detection methods showed superior performance, and could therefore complement each other. A statistical method was also developed for calibrating the cloud-free brightness temperatures to station-observed 2-m air temperatures using relationships between the means and diurnal temperature ranges of the two datasets. This enabled the identification of periods of occurrence of extreme warm air temperatures with a coefficient of determination of 0.91, and demonstrated the potential for the usage of TIR data for generating estimates of useful air temperature metrics. The efficiency of the algorithms that were used for simultaneous cloud masking and generation of cloud-free brightness temperature should be improved, in order to enable the methodology to be scaled up to a regional or global gridded level of analysis. Further work for improving operational gridded air temperature datasets by combining station-observed temperature data, modelled data from global circulation models, satellite-derived modelled cloud-free brightness temperature data and cloud masks is recommended

A review of high impact weather for aviation meteorology

This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility, aerosol/ash loading, ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can also play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in situ instruments at the surface and in the atmosphere, as well as aircraft and Unmanned Aerial Vehicles mounted sensors, are becoming more common. At smaller time and space scales (e.g., < 1 km), meteorological forecasts from NWP models need to be continuously improved for accurate physical parameterizations. Aviation weather forecasts also need to be developed to provide detailed information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges

SOFOS - A new Satellite-based Operational Fog Observation Scheme

This thesis introduces a new technique for the operational observation of fog from space. The scheme presented uses the Meteosat-8 SEVIRI system for near-real-time detection of low stratus and ground fog areas

Identification of anomalous movements of thunderstorms using radar and satellite data

Màster de Meteorologia, Facultat de Física, Universitat de Barcelona, Curs: 2014-2015, Tutors: Mª del Carme Llasat Botija, Tomeu Rigo RibasOne of the most adverse weather phenomena in Catalonia are thunderstorms producing severe weather phenomenon like heavy rainfalls, hail storms, or tornadoes. Sometimes, these thunderstorms seem to have marked paths although in some cases these trajectories vary sharply, changing completely the movement directions that have previously followed, either breaking into several cells, or joining into a bigger one. In order to identify the main features of the developing process of thunderstorms and the anomalous movements that these may in some cases follow, this paper presents a methodology that follows 3 main steps; previous classification of the events, cell identification, and finally, tracking of the cells identified. The methodology combines radar and satellite images

Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery

Climate change is producing shifts in the distribution and abundance of marine species. Such is the case of kelp forests, important marine ecosystem-structuring species whose distributional range limits have been shifting worldwide. Synthesizing long-term time series of kelp forest observations is therefore vital for understanding the drivers shaping ecosystem dynamics and for predicting responses to ongoing and future climate changes. Traditional methods of mapping kelp from satellite imagery are time-consuming and expensive, as they require high amount of human effort for image processing and algorithm optimization. Here we propose the use of mask region-based convolutional neural networks (Mask R-CNN) to automatically assimilate data from open-source satellite imagery (Landsat Thematic Mapper) and detect kelp forest canopy cover. The analyses focused on the giant kelp Macrocystis pyrifera along the shorelines of southern California and Baja California in the northeastern Pacific. Model hyper-parameterization was tuned through cross-validation procedures testing the effect of data augmentation, and different learning rates and anchor sizes. The optimal model detected kelp forests with high performance and low levels of overprediction (Jaccard's index: 0.87 +/- 0.07; Dice index: 0.93 +/- 0.04; over prediction: 0.06) and allowed reconstructing a time series of 32 years in Baja California (Mexico), a region known for its high variability in kelp owing to El Nino events. The proposed framework based on Mask R-CNN now joins the list of cost-efficient tools for long-term marine ecological monitoring, facilitating well-informed biodiversity conservation, management and decision making.LA/P/0101/2020info:eu-repo/semantics/publishedVersio