Presence Of Rain Research Articles

Validation of wind speed retrieval from HY-2B calibration microwave radiometer data during tropical cyclones

ABSTRACT In this letter, we present a validation of the wind speed retrieval from data from the calibration microwave radiometer onboard the Haiyang-2B (HY-2B) satellite against the observations from the stepped-frequency microwave radiometer (SFMR) onboard hurricane aircraft of the National Oceanic and Atmospheric Administration (NOAA) during 50 tropical cyclones (TCs). These data were collected during the cyclone season for the period from 2019 to 2022. The validation yields a root mean square error (RMSE) of 2.99 m s−1, a correlation coefficient (r) of 0.69, and a scatter index (SI) of 0.21 for the wind speed at wind speeds of <25 m s−1, which are worse than those achieved through the comparison with the HY-2B scatterometer, i.e. an RMSE of 1.15 m s−1, an r value of 0.96, and an SI of 0.11. This is probably caused by the fact that the brightness temperature measurement from the calibration microwave radiometer is likely affected by whitecaps caused by wave breaking; however, whitecaps have less influence on the sea surface backscattering signal under a regular sea state. At wind speeds of >25 m s−1, the accuracy is reduced to an RMSE of 2.19 m s−1, an r value of 0.75, and an SI of 0.07. In the presence of rain, significant distortion was observed, i.e. a variation of 3 m s−1 for a rain rate of 15 mm hr−1. This behaviour was also observed in the analysis of the HY-2B scatterometer product at low-to-moderate wind speeds. In this sense, it is believed that correction associated with rain has to be conducted in wind retrieval from HY-2B data.

SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions.

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed.

Simulation and Analysis the Attenuation Effect of Atmospheric Layers on a Laser Beam Within the Visible Range

Abstract: The power and the size of the final spot of the laser beam reaching the target are very important requirements in most of the laser applications and fields such as medical, military, and scientific, so studying laser propagation in the atmosphere is a very important topic. The propagation of the laser beam through the atmosphere is subject to several attenuation processes that deplete the power and expand the beam. Through the simulation results of the free electron laser within the visible region of the electromagnetic spectrum (400-700nm), it was found that the attenuation increases with decreasing wavelength. Laser propagation in the presence of rain and snow leads to a very large loss of power compared to propagation in normal weather conditions free of rain and snow. Atmosphere turbulence depends largely on changes in temperature, so the turbulence decreases with altitude from sea level, which makes laser work at high altitudes, such as the stratosphere, a good option with better results.&#x0D;

Factors affecting perpendicular sighting distances on shipboard line-transect surveys for cetaceans

Factors that affect cetacean perpendicular sighting distances are investigated using a Generalised Additive Modelling (GAM) framework to analyse 8,203 sightings of 34 cetacean species seen on 200,000km of shipboard line-transect surveys in the eastern Pacific in 1986-96. Perpendicular sighting distance is modelled as a non-linear function of the following predictor variables: species; an a priori species grouping; the logarithm of group size; Beaufort sea state; presence of rain or fog; sighting cue; sun glare; geographic stratum; observer; ship; year; cruise; and, in 1991-96, visibility and swell height. Based on Akaike Information Criteria (AIC), the best model for 1986-96 included all variables except rain/fog code. For the 1991-96 data, swell height anomaly was also important and replaced ship and year in the best-fit model. For independent subsets of the data, GAM coefficients were highly correlated, indicating that many of the same factors were acting in different areas and at different times. Species and sighting methods (25x binoculars vs unaided eye) had the largest effects on perpendicular sighting distances. The a priori species groups captured much, but not all of the among-species differences. Two other species-related factors (group size and sighting cue) were also important in all models. Factors related to search conditions (Beaufort sea state and swell height anomaly) and to the searchers themselves (individual observer) were also important. We anticipate that this information on the relative magnitudes of factors affecting perpendicular sighting distance can be used to improve both design and analysis of line-transect data.

3FL-Net: An Efficient Approach for Improving Performance of Lightweight Detectors in Rainy Weather Conditions

Numerous lightweight detection models have been presented in recent years, yet these detectors are inclined to develop for operating under normal weather conditions without adequate studies for rainy conditions. This is one of the causes leads drastically performance degradation of object detectors due to the decrease in visibility. To address above insufficiency, we propose a new and effective approach, named 3FL-Net, to elevate the performance of lightweight object detectors in the presence of rain. Our approach fulfills the goal by closely incorporating four subnetworks, namely feature enhancement subnetwork, feature extraction subnetwork, feature adaptation subnetwork, and lightweight detection subnetwork. The lightweight detection subnetwork achieved the accuracy improvement by learning diverse features from the feature enhancement subnetwork and feature extraction subnetwork via the feature adaptation subnetwork. To further drive the development in object detection induced by rain, we introduce a large-scale driving dataset, called iRain. The full iRain consists of 17,950 real-world rain images, which covers most of the driving scenarios and 85,081 instances explaining five prevalent object classes. Experiment results on divergent rain datasets expose that our 3FL-Net considerably improves the performance of lightweight detectors and surpasses that of the combination models between rain removal and object detection methods.

The Response of Ocean Skin Temperature to Rain: Observations and Implications for Parameterization of Rain‐Induced Fluxes

AbstractRainfall alters the physical and chemical properties of the surface ocean, and its effect on ocean skin temperature and surface heat fluxes is poorly represented in many air‐sea interaction models. We present radiometric observations of ocean skin temperature, near‐surface (5 cm) temperature from a towed thermistor, and bulk atmospheric and oceanic variables, for 69 rain events observed over the course of 4 months in the Indian Ocean as part of the DYNAMO project. We test a state‐of‐the‐art prognostic model developed by Bellenger et al. (2017, https://doi.org/10.1002/2016JC012429) to predict ocean skin temperature in the presence of rain, and demonstrate a physically motivated modification to the model that improves its performance with increasing rain rate. We characterize the vertical skin‐bulk temperature gradient induced by rain and find that it levels off at high rain rates, suggestive of a transition in skin‐layer physics that has been previously hypothesized in the literature. We also quantify the small bias that will be present in turbulent sensible heat fluxes parameterized from ocean temperature measurements made at typical “bulk” depths during a rain event. Finally, a wind threshold is observed above which the surface ocean remains well‐mixed during a rain event; however, the skin temperature is observed to decrease at all wind speeds in the presence of rain.

Updates on CYGNSS Ocean Surface Wind Validation in the Tropics

Abstract Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.

Analysis of Stopping Behavior at Rural T-Intersections Using Naturalistic Driving Study Data

Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.

Penerapan Data Mining Untuk Prediksi Perkiraan Hujan dengan Menggunakan Algoritma K-Nearest Neighbor

Rain is a condition where water droplets fall from clouds to the earth. In life, the presence of rain is highly anticipated, rain can help people who have a profession as farmers. Rain that occurs on a large scale will really provide obstacles for the community, in addition to hampering activities or activities especially those carried out on outdoor rain can also cause disaster for the community in the form of flooding. Estimating rain for the community is very important, knowing whether it will rain or not can make it easier for the community to anticipate the possibilities that may occur due to rain. However, in the process of delivering forecasts, there is often an uneven distribution of information and delays in conveying information to the public regarding whether or not rain will occur. The community should be able to independently predict whether or not rain will occur. Data processing should be done properly and correctly. Data mining is a way that can be done to assist in data processing. In this study, the settlement process will be carried out using the K-Nearest Neighbor (K-NN) algorithm. The results obtained show that the data testing decision is NO. In other words, data mining and the K-Nearest Neighbor algorithm can help the problem solving process

Characterization of noise regimes in mid-IR free-space optical communication based on quantum cascade lasers.

The recent development of Quantum Cascade Lasers (QCLs) represents one of the biggest opportunities for the deployment of a new class of Free Space Optical (FSO) communication systems working in the mid-infrared (mid-IR) wavelength range. As compared to more common FSO systems exploiting the telecom range, the larger wavelength employed in mid-IR systems delivers exceptional benefits in case of adverse atmospheric conditions, as the reduced scattering rate strongly suppresses detrimental effects on the FSO link length given by the presence of rain, dust, fog, and haze. In this work, we use a novel FSO testbed operating at 4.7 µm, to provide a detailed experimental analysis of noise regimes that could occur in realistic FSO mid-IR systems based on QCLs. Our analysis reveals the existence of two distinct noise regions, corresponding to different realistic channel attenuation conditions, which are precisely controlled in our setup. To relate our results with real outdoor configurations, we combine experimental data with predictions of an atmospheric channel loss model, finding that error-free communication could be attained for effective distances up to 8 km in low visibility conditions of 1 km. Our analysis of noise regimes may have a key relevance for the development of novel, long-range FSO communication systems based on mid-IR QCL sources.

Channel-Based Trust Model for Security in Underwater Acoustic Networks

Underwater acoustic networks are often used in mission-critical scenarios, such as military underwater networks and assets deployed for tsunami prevention; hence, an attack performed against these types of networks can easily lead to disastrous consequences. Nevertheless, countermeasures to possible network attacks have not been widely investigated so far. A reputation system, where a node gains trust each time it exhibits a good behavior, and loses trust each time it behaves suspiciously, is an effective way to identify possible attackers in the network. The main challenge when applying a reputation system in an underwater network is to understand whether the network performance degrades because a node is acting maliciously intentionally, or because of the changed channel conditions, causing a large packet drop. For instance, when a ship travels close to an underwater network deployment, it causes an increased packet loss, and so does the change of environmental conditions, such as a drop of temperature, the presence of rain or the increase of the wind speed. This behavior of the acoustic channel can be characterized with a hidden Markov model, whose parameters are obtained observing the time evolution of the acoustic channel in a sea experiment. This article presents a trust model based on the knowledge of the channel state, inferred from the perceived noise and received signal strength, in which misbehavior and correct behavior are considered differently according to the actual channel state. We evaluate the model both analytically and through simulations, implementing the trust mechanism in the DESERT Underwater Network framework.

IoT-Based Watering, Control, and Monitoring System for Tomato Plants Using Wemos D1 Mini

The growth of plant tomatoes is influenced by several factors, such as temperature, humidity, land, and rain. This study aimed to design the watering, control, and monitoring system for plants tomatoes with IoT-based using Wemos D1 Mini. The equipment is designed for sprinkling the plant tomatoes in a pot based on the response of a capacitive soil moisture sensor. The temperature was monitored using the DS18B20 sensor, and the presence of rain was determined using the raindrop sensor. On top of the equipment, an automatic on-off roof was built. This equipment was integrated with the website of Thingspeak and the Blynk application. The test results show that the design in this study could control the pump based on soil humidity with a control value of 60%. Furthermore, the built roof automatically works when detecting the temperature (higher or lower than 28°C) and the presence of rain.

Scintillation Effects of Ka-band Frequency on Satellite Application

The satellite communication system is currently congested because of the high demand for data transmission. The Ka-band is a greater range band that can help to solve the issue. However, Ka-band is severely impacted by scintillation at its high frequency. One of the transmitting impairments is scintillation, a sudden fluctuation in the amplitude of the signal and electromagnetic waves, that generates signal attenuation and degradation. In the presence of rain and even under clear skies, scintillation affects the output of Ka-Band. The scintillation prediction model has mostly been evaluated and applied in countries with four-season climates. The objective of this study was to evaluate the Ka-band scintillation data and compare the findings with other existing scintillation models to validate the outcome. For data of one year (2016) at the Ka-Band frequency of 20.2 GHz, this research focused on analysing the tropospheric scintillation from the satellite data. The experimental data from MEASAT 5 were analysed to see the effect of tropospheric scintillation under clear-sky conditions using a dish antenna with a diameter of 7.3 metres and an elevation angle of 68.8°. The satellite signal measurement samples were gathered and filtered using MATLAB to provide clear-sky scintillation. Next, the obtained raw data was converted into readable data. The data was then plotted, and the experimental data was compared to the other models of scintillation. It was essential to evaluate the outcome of the comparison and address which model was most appropriate for tropical climates. Moreover, this project’s cutoff frequency was 0.023Hz, which was computed from the average cut-off frequencies of 12 months in 2016.

Hurricane and Typhoon Storm Wind Resolving NOAA NCEI Blended Sea Surface Wind (NBS) Product

Improving forecasts of storms and hurricanes and their potential impacts is highly important to public safety, economic security, commerce, and community infrastructure. One key element of forecast improvement is more accurate and increased spatial–time coverage of observational data for model calibration, quality control and initialization, and/or data assimilation. The National Oceanic and Atmospheric Administration (NOAA) has been producing a global gridded 0.25° and 6-hourly sea surface winds product that has wide applications in marine transportation, marine ecosystem and fisheries, offshore winds, weather and ocean forecasts, and other areas. The NOAA National Centers for Environmental Information (NCEI) Blended Sea winds (NBS) v1.0 product is generated by blending observations from multiple sources (satellites), including scatterometers and microwave radiometers/imagers. However, these sensors do not provide accurate observations of intensive high-speed hurricane winds because their signals saturate in very high winds or degrade in the presence of rain. Recent advancements in satellite wind retrievals revealed that the L-band (1.42 GHz) instrument on the Soil Moisture Active Passive (SMAP) satellite and the AMSR2 All-Weather channel (~6.9 GHz) can provide accurate hurricane winds of up to 65 m/s (145 MPH) without being affected by rain; these data are incorporated in a new version of the Blended Sea Winds, NBS v2.0, using a multi-sensor data fusion technique based on random errors, enabling it to resolve very high winds, especially along the eyewalls of tropical cyclones and hurricanes. NBS v2.0 provides both a long-term record of 30+ years retrospectively since July 1987 and a near-real-time mode with 1-day latency.

Radiological consequence assessment for hypothetical nuclear explosion scenario using HotSpot

In this study the influence of atmospheric conditions, release height and particle size are studied on the radiological impact due to a hypothetical 15 KT nuclear explosion using the HotSpot model. Simulations show that among different parameters, the height of burst produces the highest impact on the total effective dose (TED). It is found that the maximum TED is about 4 orders higher for surface burst compared to elevated explosions. The range of impact of acute doses (TED >0.25 Sv) extends up to 10 km for neutral to stable conditions for ground releases and 5 km for unstable conditions for 600 m release. It has been found that city type terrain reduces the doses by 40% and 80% in unstable and stable conditions respectively compared to flat terrain. Simulations with different particle sizes indicate that the ground shine and TED are about twice higher for 100% RF particles compared to 50% RF particles after 0.5 km for all atmospheric conditions. The range of impact extends from 6 km for unstable to 10 km for stable conditions for both particle sizes. Larger particles lead to roughly 8 times higher ground deposited activity within 0.5 km for A to D conditions. For elevated release dose is highly sensitive to particle size parameter, almost 40 times higher compared to ground release and the range of impact extends to 10 km for 50% RF against 6 km for 100% RF. Simulations with inversion conditions suggest that an elevated inversion leads to enhanced TED especially for unstable condition (A, B, C classes) roughly by 2 factors at 1 km to 1 order at 10 km relative to no inversion and sensitivity of capping layer is lower for elevated releases. It has been found that presence of rain (10 mm.h−1) leads to higher groundshine dose by ∼1 order compared to no rain scenario. The sensitivity of washout is relatively high for elevated releases in the first 4 km range where the groundshine is at least 2 to 3 orders more compared to no rain condition and range of acute dose extends up to 8 km from 6 km in raining condition.

Mitigation of Rain Effect on Wave Height Measurement Using X-Band Radar Sensor

The presence of rain can negatively affect the performance of many sensors such as X-band radar. In this paper, an effective approach is proposed to mitigate the effect of rain on significant wave height ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H}_{s}$ </tex-math></inline-formula> ) estimation from X-band radar sensor data along with a machine-learning (ML)-based method. First of all, the haze removal algorithm is applied to rain-contaminated radar images as pre-processing. Then, three different features are extracted from the processed radar images. Different combinations of these three features are utilized to estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H}_{s}$ </tex-math></inline-formula> under the rain condition by using support vector regression (SVR)-based and temporal convolutional network (TCN)-based regression methods. It is found that the root-mean-square-errors (RMSEs) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H}_{s}$ </tex-math></inline-formula> estimation results using two typical methods (signal-to-noise ratio (SNR)-based and ensemble empirical mode decomposition (EEMD)-based linear fitting methods) are decreased by 0.14 m and 0.48 m after introducing the haze removal algorithm, respectively. Also, a relatively high accuracy can be achieved using the SVR-based regression method with the combination of SNR and gray level co-occurrence matrix (GLCM) features. Compared to the SNR-based and EEMD-based linear regression methods, the proposed SVR-based method further improves the estimation accuracy, with reductions of RMSE by 0.19 m and 0.82 m, respectively.

The Effect of Rainfall on the UAV Placement for 5G Spectrum in Malaysia

In this paper, the influence of rainfall on the deployment of UAV as an aerial base station in the Malaysia 5G network is studied. The outdoor-to-outdoor and outdoor-to-indoor path loss models are derived by considering the user’s antenna height, rain attenuation, and the wall penetration loss at high frequencies. The problem of finding the UAV 3D placement is formulated with the objective to minimize the total path loss between the UAV and all users. The problem is solved by invoking two algorithms, namely Particle Swarm Optimization (PSO) and Gradient Descent (GD) algorithms. The performance of the proposed algorithms is evaluated by considering two scenarios to determine the optimum location of the UAV, namely outdoor-to-outdoor and outdoor-to-indoor scenarios. The simulation results show that, for the outdoor-to-outdoor scenario, both algorithms resulted in similar UAV 3D placement unlike for the outdoor-to-indoor scenario. Additionally, in both scenarios, the proposed algorithm that invokes PSO requires less iterations to converge to the minimum transmit power compared to that of the algorithm that invokes GD. Moreover, it is also observed that the rain attenuation increases the total path loss for high operating frequencies, namely at 24.9 GHz and 28.1 GHz. Hence, this resulted in an increase of UAV required transmit power. At 28.1 GHz, the presence of rain at the rate of 250 mm/h resulted in an increase of UAV required transmit power by a factor of 4 and 15 for outdoor-to-outdoor and outdoor-to-indoor scenarios, respectively.

The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS

A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.

Relación entre el cambio meteorológico y el dolor articular en un grupo de individuos

Introduction: Joint pain can be influenced by obesity, sedentary lifestyle, rheumatic conditions, or previous joint surgeries, but many people often attribute increased joint pain to changing weather patterns. There are even individuals who claim that they can predict weather changes by their symptoms. Materials and method: observational, cross-sectional, analytical study. A survey documenting clinical variables (i.e. age, pathological history) and data related to pain and meteorological changes that could be associated with pain were documented. Descriptive statistical analysis was performed. The association between the presence of pain prior to or during the meteorological change and associated factors, was evaluated by nX2 test and the OR with its respective CI. Values less than 0.05 were considered statistically significant. Results: 147 individuals were included, 59.9% (88/147) of the respondents were female. The respondents were divided into two groups, young adult and mature adult, according to an age range. Forty six percent belonged to the young adult group and 53.74% to the ma- ture adult group. A total of 39.46% (58/147) reported pain before a meteorological change. Of the patients who reported pain, 55.5% (32/58)reported the knee as the most frequent area of pain. There was no statistically significant association between the presence of pain prior to a specific meteorological change(i.e. temperature variation or the presence of rain). However, an association was observed between the presence of pain prior to temperature change and mature adult age group (p=0.008, OR=0.398 CI= 0.20-0.79). No association was found between the presence of pain and other clinical characteristics reported by the respondents.Conclusion: The majority of respondents in the older adult group perceived joint pain prior to a change in the weather. The mature adult group manifested some pain more frequently. The young adult group mostly did not report pain prior to weather change.

Evaluation of GEOS Precipitation Flagging for SMAP Soil Moisture Retrieval Accuracy

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can either (1) produce an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain), or (2) produce an unnecessary non-retrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multi-satellite Retrievals for GPM (IMERG) Version 06 Early Run (latency of ~4 hrs) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 6 PM ascending and 6 AM descending SMAP overpasses over the first five years of the mission (2015-2020). Consisting of blended precipitation measurements from the GPM (Global Precipitation Mission) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include: i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, ii) the IMERG product has a higher frequency of light precipitation amounts, and iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs. in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement (standard deviation of the ubRMSE less than 0.04 m3/m3).