scholarly journals A mask R-CNN model for reidentifying extratropical cyclones based on quasi-supervised thought

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chuhan Lu ◽  
Yang Kong ◽  
Zhaoyong Guan
Keyword(s):  
Tropical Cyclones ◽  
Traditional Method ◽  
Target Identification ◽  
Meteorological Data ◽  
Model Performance ◽  
Extratropical Cyclones ◽  
Research Fields ◽  
Considerable Portion ◽  
Strong Winds ◽  
Applications Of Machine Learning

Abstract The applications of machine learning/deep learning (ML/DL) methods in meteorology have developed considerably in recent years. Massive amounts of meteorological data are conducive to improving the training effect and model performance of ML/DL, but the establishment of training datasets is often time consuming, especially in the context of supervised learning. In this paper, to identify the two-dimensional (2D) structures of extratropical cyclones in the Northern Hemisphere, a quasi-supervised reidentification method for extratropical cyclones is proposed. This method first uses a traditional automatic cyclone identification method to construct a trainable labeled dataset and then reidentifies extratropical cyclones in a quasi-supervised fashion by using a (pre-trained) Mask region-based convolutional neural network (Mask R-CNN) model. In comparison, the new method increases the number of identified cyclones by 8.29%, effectively supplementing the traditional method. The newly recognized cyclones are mainly shallow or moderately deep subsynoptic-scale cyclones. However, a considerable portion of the new cyclones along the coastlines of the oceans are accompanied by strong winds. In addition, the Mask R-CNN model also shows good performance in identifying the horizontal structures of tropical cyclones. The quasi-supervised concept proposed in this paper may shed some light on accurate target identification in other research fields.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

Development of a Stepped Calibration Approach for XAJ Hydrological Model

2013 ◽  
Vol 405-408 ◽  
pp. 2222-2225
Author(s):  
Qian Li ◽  
Wei Min Bao ◽  
Jing Lin Qian
Keyword(s):  
Root Mean Square Error ◽  
Objective Function ◽  
Traditional Method ◽  
Hydrological Model ◽  
Model Performance ◽  
Layer By Layer ◽  
Mean Square ◽  
Objective Functions ◽  
Single Objective ◽  
Calibration Approach

This paper discusses the conceptual stepped calibration approach (SCA) which has been developed for the Xinanjiang (XAJ) model. Multi-layer and multi-objective functions which can make optimization work simpler and more effective are introduced in this procedure. In all eight parameters were considered, they were divided into four layers according to the structure of XAJ model, and then calibrated layer by layer. The SCA procedure tends to improve the performance of the traditional method of calibration (thus, using a single objective function, such as root mean square error RMSE). The compared results demonstrate that the SCA yield better model performance than RMSE.

Potential of increasing intensity of tropical cyclones due to sea surface temperature and impact on coral reefs in the context of climate change

2021 ◽  
Vol 14 (9) ◽  
pp. 1-7
Author(s):  
N.D. Hung ◽  
L.T.H. Thuy ◽  
T.V. Hang ◽  
T.N. Luan
Keyword(s):  
Climate Change ◽  
Coral Reefs ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Coral Cover ◽  
Central Zone ◽  
Biosphere Reserve ◽  
Sea Surface ◽  
Strong Winds

The coral reef ecosystem in Cu Lao Cham, Vietnam is part of the central zone of the Cu Lao Cham -Hoi An, a biosphere reserve and it is strictly protected. However, the impacts of natural disasters - tropical cyclones (TCs) go beyond human protection. The characteristic feature of TCs is strong winds and the consequences of strong winds are high waves. High waves caused by strong TCs (i.e. level 13 or more) cause decline in coral cover in the seas around Cu Lao Cham. Based on the relationship between sea surface temperature (SST) and the maximum potential intensity (MPI) of TCs, this research determines the number of strong TCs in Cu Lao Cham in the future. Using results from a regional climate change model, the risk is that the number of strong TCs in the period 2021-2060 under the RCP4.5 scenario, will be 3.7 times greater than in the period 1980-2019 and under the RCP 8.5 scenario it will be 5.2 times greater than in the period 1980-2019. We conclude that increases in SST in the context of climate change risks will increase the number and intensity of TCs and so the risk of their mechanical impact on coral reefs will be higher leading to degradation of this internationally important site.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

Compound Events of Tropical Cyclone and Flooding in India

2021 ◽  
Author(s):  
Akshay Rajeev ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Tropical Cyclones ◽  
Heavy Rain ◽  
Disaster Mitigation ◽  
Infiltration Capacity ◽  
Monsoon Period ◽  
Antecedent Soil Moisture ◽  
Vic Model ◽  
Compound Events ◽  
Strong Winds

<p>India is severely affected by tropical cyclones (TC) each year, which generates intense rainfall and strong winds leading to flooding. Most of the TC induced floods have been attributed to heavy rain associated with them. Here we show that both rainfall and elevated antecedent soil moisture due to temporally compounding tropical cyclones cause floods in the major Indian basins. We assess each basin's response to observed TC events from 1980 to 2019 using the Variable Infiltration Capacity (VIC) model. The VIC model was calibrated (R2 > 0.5) and evaluated against observed hourly streamflow for major river basins in India. We find that rainfall due to TC does not result in floods in the basin, even for rainfall intensities similar to the monsoon period. However, TCs produce floods in the basins, when antecedent soil moisture was high. Our findings have implications for the understanding of TC induced floods, which is crucial for disaster mitigation and management.</p>

scholarly journals An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 4 (1) ◽  
pp. 13-21 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water ◽  
Snowpack Properties

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

scholarly journals A Climatological Study of Western Mediterranean Medicanes in Numerical Simulations with Explicit and Parameterized Convection

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 397 ◽  
Author(s):  
Francesco Ragone ◽  
Monica Mariotti ◽  
Antonio Parodi ◽  
Jost von Hardenberg ◽  
Claudia Pasquero
Keyword(s):  
Mediterranean Basin ◽  
Regional Model ◽  
Western Mediterranean ◽  
Weather Research And Forecasting ◽  
Historical Period ◽  
Extratropical Cyclones ◽  
High Mountains ◽  
Western Mediterranean Basin ◽  
Cyclone Tracking ◽  
Strong Winds

The semi-enclosed Mediterranean basin, surrounded by high mountains, is placed in a favorable location for cyclonic storms development. Most of these are extratropical cyclones of baroclinic and orographic origin, but occasionally, some low pressure systems may develop to assume features characteristic of tropical cyclones. Medicanes (MEDIterranean hurriCANES) are infrequent and small-sized tropical-like cyclones. They originate and develop over sea, and are associated with strong winds and heavy precipitations. Proper definitions and classifications for Medicanes are still partially lacking, and systematic climatic studies have appeared only in recent years. In this work, we provide climatologies of Medicanes in the Western Mediterranean basin based on multidecadal runs performed with the Weather Research and Forecasting regional model with different resolutions and setups. The detection of Medicanes is based on a cyclone tracking algorithm and on the methodology of Hart cyclone phase space diagrams. We compare the statistics of Medicanes in the historical period 1979–1998 between runs at a resolution of 11 km with different convective parameterizations and microphysics schemes and one run at a resolution of 4 km with explicitly resolved convection. We show how different convective parameterization schemes lead to different statistics of Medicanes, while the use of different microphysical schemes impacts the length of the cyclone trajectories.

scholarly journals Meteorological Conditions at Racetrack Playa, Death Valley National Park: Implications for Rock Production and Transport

2011 ◽  
Vol 50 (12) ◽  
pp. 2361-2375 ◽  
Author(s):  
Ralph D. Lorenz ◽  
Brian K. Jackson ◽  
Jason W. Barnes ◽  
Joseph N. Spitale ◽  
Jani Radebaugh ◽  
...  
Keyword(s):  
National Park ◽  
Probability Distributions ◽  
Meteorological Data ◽  
Time Lapse ◽  
Meteorological Conditions ◽  
Death Valley ◽  
Trail Formation ◽  
Strong Winds ◽  
Death Valley National Park

AbstractThree decades of weather records at meteorological stations near Death Valley National Park are analyzed in an attempt to gauge the frequency of conditions that might form and erase the famous trails of wind-blown rocks in the mud of Racetrack Playa. Trail formation requires the playa to be wet, followed by strong winds and/or freezing conditions. Weather records are compared with a limited set of meteorological data that were acquired in situ at the playa over three winters and that indicate freezing on 50, 29, and 15 nights during the winters of 2007/08–09/10, respectively, as well as with the hydrological condition of the playa as determined by time-lapse cameras that observed flooding over ~1, ~5, and ~40 days, respectively, during those winters. Measurements at the nearby Panamint and Hunter Mountain stations are found to be a useful, if imperfect (~50%), indicator of Racetrack Playa conditions and give some features of Racetrack Playa’s micrometeorological behavior. Wind speed probability distributions suggest that winds that are fast enough to cause unassisted rock motion are rare and therefore that freezing of water on the playa has a role in a significant fraction of movement events.

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