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.

Soil Moisture Profiles of Unsaturated Colluvial Slopes Susceptible to Rainfall-Induced Landslides

In this work, we describe soil moisture profiles related to typical colluvial slopes that were involved in rainfall-induced shallow failures occurring in alpine and pre-alpine areas of the Friuli Venezia Giulia Region (NE Italy). The trend of the volumetric water content (θw) showed a general increase from the ground surface to the bottom soil layer, with two or three marked moisture peaks. The saturation degree (S) varied from 65–70% (topsoil horizon) to nearly saturated basal colluvium (S = 95–100%). Soil moisture data demonstrates that, for a very humid climate, colluvial covers are often close to the saturation condition for most of the year. The calculated suction profiles indicated that maximum values ranging from 40 to 55 kPa often occur in the slope surficial soil (depth < 0.2–0.5 m). This negative pore-water pressure greatly decreases after a heavy rainfall event because of the infiltration process. Complete saturation of colluvial cover in the alpine and pre-alpine regions generally requires rainfall exceeding 150–200 mm for a 24-h storm duration. This results in a recurrence time of Tr ≅ 5–10 years for critical rainfall episodes involving colluvial slopes in the Friuli Venezia Giulia Region. The case histories analyzed demonstrate the importance of performing a detailed lithostratigraphic analysis of the colluvial deposit in order to properly define the suction measurement points, which there should be more of than the three-point determinations usually reported in the literature (for example, z = 0.5, 1.0 and 1.5 m).

Temperature profile measurement applications of moving webs and roll structures with intelligent roll embedded sensor technology

An intelligent roll for sheet and roll cover temperature profiles is a mechatronic system consisting of a roll in a web handling machine that is also used as a transducer for sensing cross-machine direction (CD) profiles. The embedded temperature sensor strips are mounted under or inside the roll cover, covering the full width of the roll’s cross-dimensional length. The sensor system offers new opportunities for online temperature measurement through exceptional sensitivity and resolution, without adding external measurement devices. The measurement is contacting, making it free from various disturbances affecting non-contacting temperature measurements, and it can show the roll cover’s internal temperatures. This helps create applications that have been impossible with traditional technology, with opportunities for process control and condition monitoring. An application used for process analysis services without adding a roll cover is made with “iRoll Portable Temperature” by mounting the sensor on the shell in a helical arrangement with special taping. The iRoll Temperature sensors are used for various purposes, depending on the application. The two main targets are the online temperature profile measurement of the moving web and the monitoring of the roll covers’ internal temperatures. The online sheet temperature profile has its main utilization in optimizing moisture profiles and drying processes. This enables the removal of speed and runnability bottlenecks by detecting inadequate drying capacity across the sheet CD width, the monitoring condition of the drying equipment, the optimization of drying energy consumption, the prevention of unnecessary over-drying, the optimization of the float drying of coating colors, and the detection of reasons for moisture profile errors. This paper describes this novel technology and its use cases in the paper, board, and tissue industry, but the application can be extended to pulp drying and industries outside pulp and paper, such as the converting and manufacture of plastic films.

Similarity solutions for early-time constant boundary flux imbibition in foams and soils

The foam drainage equation and Richards equation are transport equations for foams and soils, respectively. Each reduces to a nonlinear diffusion equation in the early stage of infiltration during which time, flow is predominantly capillary driven, hence is effectively capillary imbibition. Indeed such equations arise quite generally during imbibition processes in porous media. New early-time solutions based on the van Genuchten relative diffusivity function for soils are found and compared with the same for drainage in foams. The moisture profiles which develop when delivering a known flux into these various porous materials are sought. Solutions are found using the principle of self-similarity. Singular profiles that terminate abruptly are obtained for soils, a contrast with solutions obtained for node-dominated foam drainage which are known from the literature (the governing equation being now linear is analogous to the linear equation for heat transfer). As time evolves, the moisture that develops at the top boundary when a known flux is delivered is greater in soils than in foams and is greater still in loamy soils than in sandstones. Similarities and differences between the various solutions for nonlinear and linear diffusion are highlighted. Graphic abstract

Sensitivity of land-atmosphere coupling strength to perturbations of early-morning temperature and moisture profiles in the European summer

Land-atmosphere coupling can have a crucial impact on convective initiation. Yet, uncertainty remains in the analyses of the atmospheric segment of the coupling between land surface wetness and the triggering of deep moist convection, particularly over Europe. One reason for this is a lack of suitable data. To overcome this limitation, we perturb early-morning temperature and moisture profiles from a regional climate simulation covering the period 1986–2015 over Europe to create a spread in atmospheric conditions. Applying the ‘Convective Triggering Potential – low-level Humidity Index’ framework, we analyze whether and how strongly the coupling strength and the predominance of positive versus negative feedbacks are sensitive to modifications in the atmospheric conditions. The results show that the hotspot region in northeastern Europe, in which strong feedbacks are likely to occur, is insensitive to temperature and moisture changes, but the number of potential feedback days varies by up to 20 days per season in dependence of the atmospheric background conditions. Temperature modifications rather control differences in the coupling strength in the north of the domain, while moisture changes dominant the control in the south. In the north of the hotspot region, a predominance for positive feedbacks (deep convection over wet soils) remains, but a switch of the dominant feedback class between positive feedbacks and a transition zone (convection over any soil, but usually shallow convection) occurred from the Alps to around the Black Sea. This indicates a dependence of the dominant feedback class on temperature and relative humidity in this region.

Application of Spatial Time Domain Reflectometry for Investigating Moisture Content Dynamics in Unsaturated Loamy Sand for Gravitational Drainage

The strength of unsaturated soil is defined by the soil water retention behavior and soil suction acting inside the soil matrix. In order to obtain the suction and moisture profile in the vadose zone, specific measuring techniques are needed. Time domain reflectometry (TDR) conventionally measures moisture at individual points only. Therefore, spatial time domain reflectometry (spatial TDR) was developed for characterizing the moisture content profile along the unsaturated soil strata. This paper introduces an experimental set-up used for measuring dynamic moisture profiles with high spatial and temporal resolution. The moisture measurement method is based on inverse modeling the telegraph equation with a capacitance model of soil/sensor environment using an optimization technique. With the addition of point-wise soil suction measurement using tensiometers, the soil water retention curve (SWRC) can be derived in the transient flow condition instead of the static or steady-state condition usually applied for conventional testing methodologies. The experiment was successfully set up and conducted with thorough validations to demonstrate the functionalities in terms of detecting dynamic moisture profiles, dynamic soil suction, and outflow seepage flux under transient flow condition. Furthermore, some TDR measurements are presented with a discussion referring to the inverse analysis of TDR traces for extracting the dielectric properties of soil. The detected static SWRC is finally compared to the static SWRC measured by the conventional method. The preliminary outcomes underpin the success of applying the spatial TDR technique and also demonstrate several advantages of this platform for investigating the unsaturated soil seepage issue under transient flow conditions.

Machine Learning Methods to Infer Precipitation Phase from Temperature and Moisture Profiles

&lt;p&gt;The phase of falling precipitation can have a large societal impact for both hydrology (snow storage, rain-on-snow events), meteorology (snowstorms, freezing rain) and climate (snow albedo feedback). In Canada, many surface weather stations report precipitation information in the form of total precipitation (liquid-equivalent), but very few weather stations directly report snow. Thus, precipitation phase must be inferred from ancillary data such as temperature and moisture. Each scientific community has developed its own tool for the determination phase in the absence of direct observations: from simple rules based on air temperature, dew point temperature or wet bulb temperature to sophisticated microphysics schemes passing by methods based on the discrimination of features extracted from vertical temperature profiles. With the recent advances of machine learning, there is an opportunity to investigate another set of methods based on deep neural networks.&lt;/p&gt;&lt;p&gt;Using ERA5 and ERA5-Land model re-analyses as the reference, we trained several recurrent neural networks (RNN) on vertical profiles of temperature and moisture to infer the snow fraction &amp;#8211; the ratio of solid precipitation to total precipitation. Since precipitation phase (solid, liquid or mixed) was not directly available in the model re-analysis, we defined it using two thresholds: snow fraction of less than 5% for liquid, snow fraction of more than 95% for solid phase, and mixed phase for everything in between. The best performing neural network for regressing snow fraction is found to be a Gated Recurrent Unit (GRU) RNN using profiles up to 500 hPa above the surface of both temperature and relative humidity. A slight decrease in performance is observed if profiles up to 700 hPa are used instead. A feature experiment also reveals that the performance is significantly better when using both temperature and moisture profiles, but it does not really matter what type of moisture observations are used (either dew point spread, wet bulb temperature or relative humidity). For classifying precipitation phase, the balanced accuracy is over 90%, clearly outperforming the implementation of Bourgouin&amp;#8217;s method used operationally in part of Canada. Compared with the K-Nearest Neighborhood (KNN) method trained on surface observations only, it is seen that the greatest gain in performance for GRU-RNN is when the surface temperature is close to zero degrees Celsius.&lt;/p&gt;&lt;p&gt;These preliminary results indicate the great potential of the proposed algorithm for determining snow fraction and precipitation phase in the absence of direct observations. The proposed algorithm could potentially be used for inferring snow fraction and precipitation phase in several applications such as (1) precipitation analysis for forcing hydrological models, (2) weather nowcasting, (3) weather forecast post-processing and (4) climate change impact studies.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;