Method for Forecasting Aircraft Icing Zones Based on Radiometric Measurements of Atmospheric Parameters

Icing is a potentially hazardous weather events for aviation. Aircraft icing, as a rule, occurs as a result of the formation of ice on its surface at negative temperatures and high air humidity. Automated nowcasting system “Neva” implements a method for predicting aircraft icing zones based on radiometric measurements of atmospheric parameters. The results of nowcasting of icing zones in the area of the St. Petersburg airfield (Pulkovo) at heights up to 1 kilometer on the basis of remote measurements are presented. The accuracy of forecasting was verified using information on the actual icing cases according to the data of the aerodrome weather service, as well as the data of radio sounding at the Voeikovo aerological station. The high accuracy of forecasts and the icing warning rate are shown, as well as good agreement of the nowcating results with the Godske method generally used in operational practice. Evaluation of the quality of nowcasting shows to the prospects of using the automated system “Neva” for predicting aircraft icing zones on the base of radiometric measurements of atmospheric parameters. The forecasts obtained for the period from October 2018 to March 2020 in the Pulkovo airfield area feature high accuracy and warning rate, and the results largely coincide with the generally used method for forecasting aircraft icing zones (Godske method).

Improvement of radiation modelling during cloudy sky days using in-situ and satellite measurements.

<p>Solar radiation modelling is important for the evaluation and deployment of solar renewable energy systems. The amount of solar radiation reaching the ground is influenced by geographical parameters (seasons, latitude and local characteristics of the site) and meteorological and atmospheric parameters (like humidity, clouds or particles). Those parameters have important spatio-temporal variations that make solar radiation hard to model. </p><p>Various radiation models exist in literature. Among them, the 1D radiation model part of the computational fluid dynamics software “Code_Saturne” estimates the global and direct solar irradiances at the ground. It takes into account the impact of meteorology, atmospheric gas, particles and clouds whose influence is represented using the two-stream approximation. </p><p>The model showed satisfactory results during clear-sky days  but not during cloudy-sky days. It is a common problem in solar radiation modelling, because of the complexity to accurately represent  clouds, which are extremely variable in space and time and have a strong influence on the depletion of solar irradiance.  </p><p>In the current study, the estimation of radiation during cloudy-sky days is improved by coupling the 1D radiation model of Code_Saturne with on-site and satellite measurements of the cloud optical properties. Meteorological data are obtained from the Weather Research and Forecasting (WRF) model, aerosol’s concentrations from the air-quality modelling platform Polyphemus, and on-site measurements from the SIRTA observational site (close to Paris). Two periods are simulated: 'august 2009' and 'year 2014'. It is shown that the introduction of the measured cloud properties in the computation of the surface radiation fluxes leads to a strong reduction of the simulated errors, compared to the case where these properties are derived from the WRF model.                    </p><p>A sensitivity analysis on the parameters representing clouds in the model is conducted. It enabled us to identify the most influencing parameters - cloud optical thickness (COD) and cloud fraction - and instruments that are sufficient and mandatory for a good description of solar radiation during cloudy-sky days. A fitted model is developed to deduce the COD from liquid water path measurements. Satellite and radiometric measurements could both be used, although satellite measurements are not always available.  For the estimation of cloud fraction, the best results are obtained from shortwave radiometric measurements or from a sky imager. Moreover, large error cases in hourly values of solar fluxes are examined to understand their origin. For a large part of these error cases, there is a high variation within the hour of satellite or in situ measurements, or the presence of low clouds (in more than 50% of these cases in august 2009). </p><p> </p>

Hydrological variability and suspended particulate matter in the middle river Niger bassin

<p>In the Sahel, climate variability and high population growth have led to changes in surface conditions that resulted in increased runoff coefficients and discharge in the major Sahelian rivers. The mid reaches of the Niger river have experienced significant increases in the Red flood, or local flood, that occurs during the rainy season between June and September, relative to Black flood, or Guinean flood that arrives in Niamey from December onwards.<br>The objective of this work was to characterize suspended particulate matter (SPM) during the Red and Black floods in the Niamey area and analyse their spatio-temporal dynamics. Two approaches are used : the first one consists of regular in-situ measurements of SPM concentration and in their physical and mineral characterization by electron microscopy; the second is based on monitoring water color by both in-situ and satellite (Sentinel 2) radiometric measurements.<br>SPM are characterized by very fine particles (with a major mode around 0.1-0.2 micrometers) mainly composed by kaolinites (iron oxides are also observed during the Red flood). This, combined with the very high levels of SPM concentration reached during the rainy season, results in very high values of reflectance in the visible end infrared bands. Radiometric measurements in the nir band by both the in-situ SKYE sensor and the Sentinel2 sensor are found to be significantly correlated to in-situ SPM, allowing efficient monitoring of SPM concentration in time and space.<br>SPM-discharge curves, reveal a complex relationship : SPM increases very rapidly at the beginning of the rainy season when soils are washed out after the long dry period, reaching a peak before the first discharge peak (Red flood). SPM continues to decrease during the second discharge peak (Black flood) from December to February, providing a distinct and unique signature. Analysis of satellite data allowed identifying the main sources of SPM and to quantify the significant contribution of the right bank river tributaries to sediments in the middle Niger river bassin. This contribution may further increase in the context of global changes (climate and anthropogenic) with important consequences on sediment transport but also on water quality and bacterial concentration which are strongly influenced by high SPM.</p>

Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes

Ten years of L-Band radiometric measurements have proven the capability of satellite Sea Surface Salinity (SSS) to resolve large scale to mesoscale SSS features in tropical to subtropical ocean. In mid to high latitude, L-Band measurements still suffer from large scale and time systematic errors. Here, a simple method is proposed to mitigate the large scale and seasonal varying biases. First, an Optimal Interpolation (OI) using a large correlation scale (~500 km) is used to map independently Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) Level 3 data. The mapping is compared to the equivalent mapping of in situ observations to estimate the large scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products, and increases the signal to noise ratio of the absolute SSS estimates, reducing the RMSD of in situ-satellite products by about 26-32% from mid to high latitude, respectively, in comparison to the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to e.g. radio frequency interferences, land-sea contamination, ice-sea contamination remain challenging to reduce given the low sensitivity of L-Band radiometric measurements to SSS in cold water. Using the thermodynamic equation of state (TEOS-10), the resulting L4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful are these satellite derived parameters to fully characterize the surface ocean water masses at large mesoscale.