A case study of the impact of INSAT derived humidity profiles on precipitation forecast by limited area model

The impact of humidity profiles estimated from INSAT digital IR cloud imagery data on initial moisture analysis in the IMD's operational limited area forecast system has been investigated. Method for assimilation of humidity profiles data as pseudo observations in the analysis scheme has been developed and implemented in the regional analysis scheme. Verification of humidity analysis with this data has shown substantial improvements in the moisture analysis over the data sparse region of tropics. Impact of the improved humidity analysis on model predicted rainfall is examined. The experiments show improved rainfall prediction.

Estimation of relative humidity profiles From INSAT cloud data

Accurate humidity profiles are needed for obtaining useful rainfall forecasts from numerical weather prediction models. In this context objective estimation of moisture profiles over ocean areas using satellite cloud data becomes important. For this purpose the fractional cloudiness data available from INSAT has been classified into different cloud categories depending on the total cloud amount and the levels at which the clouds have been present. Actual relative humidity profiles have been obtained using TEMP data of Port Blair (11 .6°N 92.7°E) and Minicoy (8,3°N, 72,9°E), Most frequently occurring relative humidity profile has been selected as being representative of humidity distribution in the vertical for a given cloud category. The preliminary results reported here show that these bogus relative humidity profiles could provide useful Information on moisture distribution in the vertical over the Indian Ocean.

Winter thermodynamic vertical structure in the Arctic atmosphere linked to large-scale circulation

Thermodynamic profiles are affected by both the large-scale dynamics and the local processes, such as radiation, cloud formation and turbulence. Based on ERA5 reanalysis, radiosoundings and cloud cover observations from winters 2009–2018, this study demonstrates manifold impacts of large-scale circulation on temperature and specific humidity profiles in the circumpolar Arctic north of 65∘ N. Characteristic wintertime circulation types are allocated using self-organizing maps (SOMs). The study shows that influence of different large-scale flows must be viewed as a progressing set of processes: (1) horizontal advection of heat and moisture, driven by circulation, lead to so-called first-order effects on thermodynamic profiles and turbulent surface fluxes, and (2) the advection is followed by transformation of the air through various physical processes, causing second-order effects. An example of second-order effects is the associated cloud formation, which shifts the strongest radiative cooling from the surface to the cloud top. The temperature and specific humidity profiles are most sensitive to large-scale circulation over the Eurasian land west of 90∘ E and the Arctic Ocean sea ice, whereas impacts over North America and Greenland are more ambiguous. Eurasian land, between 90 and 140∘ E, occasionally receives warm and moist air from the northern North Atlantic, which, with the support of radiative impacts of clouds, weakens the otherwise strong temperature and specific humidity inversions. Altitudes of maximum temperature and specific humidity in a profile and their variability between the circulation types are good indicators of the depth of the layer impacted by surface–atmosphere processes interacting with the large-scale circulation. Different circulation types typically cause variations of a few hundred metres to this altitude, and the layer impacted is deepest over north-eastern Eurasia and North America.

An Improved Method Combining CNN and 1D-Var for the Retrieval of Atmospheric Humidity Profiles from FY-4A/GIIRS Hyperspectral Data

FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical detector in geostationary orbit. Compared to other similar instruments, it has the advantages of high temporal resolution and stationary relative to the ground. Based on the characteristics of GIIRS observation data, we proposed a humidity profile retrieval method. We fully utilized the information provided by the observation and forecast data, and used the two-dimensional brightness temperature data with the dimension of time and optical spectrum as the input of the CNN (convolution neural network model). Then, the obtained brightness temperature data were shown to be more suitable as the input for the physical retrieval method for humidity than the conventional correction method, improving the accuracy of humidity profile retrieval. We performed two comparative experiments. The first experiment results indicate that, compared to ordinary linear correction and ANN (artificial neural network algorithm) correction, our revised observed brightness temperature data are much closer to the simulated brightness temperature obtained by inputting ERA5 reanalysis data into RTTOV (Radiative Transfer for TOVS). The results of the second experiment indicate that the accuracy of the humidity profile retrieved by our method is higher than that of conventional ANN and 1D-Var (one-dimensional variational algorithm). With ERA5 reanalysis data as the reference value, the RMSE (Root Mean Squared Error) of the humidity profiles by our method is less than 8.2% between 250 and 600 hPa. Our method holds the unique advantage of the high temporal resolution of GIIRS, improves the accuracy of humidity profile retrieval, and proves that the combination of machine learning and the physical method is a compelling idea in the field of satellite atmospheric remote sensing worthy of further exploration.

The impacts of COVID-19 pandemic on the hygrothermal environment of our homes

In 2020 the residential sector witnessed a complete transformation of the way people live and occupy the spaces. Indeed, different Countries introduced total lockdowns as a measure to contain and prevent the spread of COVID-19, forcing people to stay at home. These measures impact the indoor hygrothermal environment: higher internal thermal loads and moisture generation rate may create the perfect situation to support mould growth. This project aims to understand the impacts of increased work-from-home practices on the hygrothermal performance of residential buildings. The assessment uses a two-step methodology: firstly, whole building transient simulations (software trnsys) are used to generate the indoor temperature and humidity profiles, secondly hygrothermal transient simulations (software WUFI) are used to quantify the risk of mould growth. This research reveals the inadequacy of current design and construction practices to support flexible occupation patterns.

Impact of the 3D morphology on the hygro-thermal transfer of hygroscopic materials: application to spruce wood

Spruce wood is a bio-based material that is well known in the building construction field because of its good thermal and acoustic properties. It has a heterogeneous anatomical structure and also hygroscopic nature which offers the possibility to swell or shrink–in accordance to–relative humidity solicitations. In this context, the aim of this paper is to investigate the influence of the microstructure of spruce wood on the mechanisms of heat and mass transfers. The novelty of this article is that a real 3D spruce wood structure is taken into account to model hygrothermal transfer within the material. A 3D X-ray micro-tomography was investigated for the reconstruction of the material at a resolution of 3.35 μm/pixel. Hygrothermal model was developed in order to predict the influence of the anatomical structure of wood on the material behaviour. The resulting 3D temperature and relative humidity profiles show a significant dependence on the morphological structure of the material and the mechanisms that are at the microscopic scale have an influence on the macroscopic scale.

Measurement of Solar Absolute Brightness Temperature Using a Ground-Based Multichannel Microwave Radiometer

Ground-based multichannel microwave radiometers (GMRs) can observe the atmospheric microwave radiation brightness temperature at K-bands and V-bands and provide atmospheric temperature and humidity profiles with a relatively high temporal resolution. Currently, microwave radiometers are operated in many countries to observe the atmospheric temperature and humidity profiles. However, a theoretical analysis showed that a radiometer can be used to observe solar radiation. In this work, we improved the control algorithm and software of the antenna servo control system of the GMR so that it could track and observe the sun and we use this upgraded GMR to observe solar microwave radiation. During the observation, the GMR accurately tracked the sun and responded to the variation in solar radiation. Furthermore, we studied the feasibility for application of the GMR to measure the absolute brightness temperature (TB) of the sun. The results from the solar observation data at 22.235, 26.235, and 30.000 GHz showed that the GMR could accurately measure the TB of the sun. The derived solar TB measurements were 9950 ± 334, 10,351 ± 370, and 9217 ± 375 K at three frequencies. In a comparison with previous studies, we obtained average percentage deviations of 9.1%, 5.3%, and 4.5% at 22.235, 26.235, and 30.0 GHz, respectively. The results demonstrated that the TB of the sun retrieved from the GMR agreed well with the previous results in the literature. In addition, we also found that the GMR responded to the variation in sunspots and a positive relationship existed between the solar TB and the sunspot number. According to these results, it was demonstrated that the solar observation technique can broaden the field usage of GMR.

Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite

The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS) onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance.

Retrieval of the Atmospheric Temperature and Humidity Profiles Using a Feed-Forward Neural Network

Using a feed-forward neural network, an inverse algorithm was developed to profile the vertical structure of temperature and specific humidity. The inverse algorithm (inverse model) was used to calculate temperature and humidity profiles, which were then compared with other existing methods. The inverse model is found efficient in profiling the vertical structure of temperature and humidity as compared to other existing methods. For example, the statistical methods notorious for their high computational cost, altitude-dependent error, and inability to accurately retrieve the vertical temperature and humidity profiles, are enhanced with an inverse model. The inverse model’s diurnal and seasonal cycle profiles are also found superior to those of other existing methods, which could be useful for assimilation in numerical weather forecast models. We suggest that incorporating such an inverse model into the ground-based microwave radiometer (GMWR) will enhance the accuracy of the vertical structure of temperature and humidity profiles, and so the improvement in weather forecasting. The developed inverse model has a resolution of 50 m between the surface to 500 m and 100 m between 500–2000 m, and 500 m beyond 2000 m.