scholarly journals Snow and weather climatic control on snow avalanche occurrence fluctuations over 50 yr in the French Alps

2012 ◽  
Vol 8 (2) ◽  
pp. 855-875 ◽  
Author(s):  
H. Castebrunet ◽  
N. Eckert ◽  
G. Giraud
Keyword(s):  
Regression Models ◽  
Snow Avalanche ◽  
Weather Data ◽  
Climate Data ◽  
Winter Climate ◽  
Continuous Increase ◽  
Climate Fluctuations ◽  
French Alps ◽  
Weather Patterns ◽  
Short Period

Abstract. Snow avalanche activity is controlled to a large extent by snow and weather patterns. However, its response to climate fluctuations remains poorly documented. Previous studies have focused on direct extraction of trends in avalanche and winter climate data, and this study employs a time-implicit method to model annual avalanche activity in the French Alps during the 1958–2009 period from its most representative climatic drivers. Modelled snow and weather data for different elevations and aspects are considered as covariates that explain actual observed avalanche counts, modelled instability indexes, and a combination of both avalanche activity indicators. These three series present relatively similar fluctuations over the period and good consistency with historically harsh winters. A stepwise procedure is used to obtain regression models that accurately represent trends as well as high and low peaks with a small number of physically meaningful covariates, showing their climatic relevance. The activity indicators and their regression models seen as time series show, within a high interannual variability, a predominant bell-shaped pattern presumably related to a short period of colder and snowier winters around 1980, as well as a very slight but continuous increase between 1975 and 2000 concomitant with warming. Furthermore, the regression models quantify the respective weight of the different covariates, mostly temperature anomalies and south-facing snowpack characteristics to explain the trends and most of the exceptional winters. Regional differences are discussed as well as seasonal variations between winter and spring activity and confirm rather different snow and weather regimes influencing avalanche activity over the Northern and Southern Alps, depending on the season.

scholarly journals Snow and weather climatic control on snow avalanche occurrence fluctuations over 50 yr in the French Alps

2011 ◽  
Vol 7 (6) ◽  
pp. 4173-4221
Author(s):  
H. Castebrunet ◽  
N. Eckert ◽  
G. Giraud
Keyword(s):  
Regression Models ◽  
Snow Avalanche ◽  
Weather Data ◽  
Climate Data ◽  
Winter Climate ◽  
Continuous Increase ◽  
Climate Fluctuations ◽  
French Alps ◽  
Weather Patterns ◽  
Short Period

Abstract. Snow avalanche activity is controlled to a large extent by snow and weather patterns. However, its response to climate fluctuations remains poorly documented. Previous studies have focused on direct extraction of trends in avalanche and winter climate data, and this study employs a time-implicit method to model annual avalanche activity in the French Alps during the 1958–2009 period from its most representative climatic drivers. Modeled snow and weather data for different elevations and aspects are considered as covariates that explain actual observed avalanche counts, modeled instability indexes, and a combination of both avalanche activity indicators. These three series present relatively similar fluctuations over the period and good consistency with historically harsh winters. A stepwise procedure is used to obtain regression models that accurately represent trends as well as high and low peaks with a small number of physically meaningful covariates, showing their climatic relevance. The activity indicators and their regression models seen as time series show, within a high interannual variability, a predominant bell-shaped pattern presumably related to a short period of colder and snowier winters around 1980, as well as a very slight but continuous increase between 1975 and 2000 concomitant with warming. Furthermore, the regression models quantify the respective weight of the different covariates, mostly temperature anomalies and south-facing snowpack characteristics to explain the trends and most of the exceptional winters. Regional differences are discussed as well as seasonal variations between winter and spring activity and confirm rather different snow and weather regimes influencing avalanche activity over the Northern and Southern Alps, depending on the season.

scholarly journals Assimilating Weather Data into a Digital Event Gazetteer of Airborne Parachute Operations during the French Indochina War

2017 ◽  
Vol 10 (1) ◽  
pp. 19-34
Author(s):  
Johnathan P. Kirk ◽  
Gordon A. Cromley
Keyword(s):  
Two Dimensions ◽  
Weather Data ◽  
Climate Data ◽  
Historical Events ◽  
Reanalysis Dataset ◽  
Climate Conditions ◽  
Synoptic Classification ◽  
Weather Patterns ◽  
Weather And Climate ◽  
Indochina War

Abstract Modern datasets cataloging historical events, known as digital event gazetteers, feature spatiotemporal data regarding events that enable analysis through parameters including location and other descriptive information of those events. Weather and climate data represent two dimensions of spatiotemporal information, which can enhance understanding of historical events. A recently published digital event gazetteer of airborne parachute operations [opérations aéroportées (OAPs)] during and prior to the French Indochina War, spanning from 1945 to 1954, represents an opportunity to associate discrete historical events with weather information. This study outlines a methodology for assimilating weather data into the construct of a digital event gazetteer and then demonstrates example analyses of how the weather and climate conditions in Indochina may relate to OAPs during the war. A synoptic classification, utilizing the self-organizing maps procedure, is performed using daily mean sea level pressure data from 1945 to 2010, from a twentieth-century reanalysis dataset, to characterize weather patterns over the Indochina Peninsula. Since observations are sparse during the years of the conflict, the resulting weather patterns are associated with modern precipitation observations in the area, as a representation of wet and dry patterns during the war. The appropriate daily weather pattern is then assigned to each OAP in order to investigate its relationship with the weather and climate patterns of Indochina, including the influence of monsoon seasons, and how the resulting precipitation patterns affected combat operations across the theater. Additionally, specific OAPs of various missions are analyzed to investigate how weather patterns may have affected operation planning during the French Indochina War.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing

2021 ◽  
Vol 13 (13) ◽  
pp. 2548
Author(s):  
Luthfan Nur Habibi ◽  
Tomoya Watanabe ◽  
Tsutomu Matsui ◽  
Takashi S. T. Tanaka
Keyword(s):  
Spatial Variability ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Plant Density ◽  
Density Measurement ◽  
Critical Factor ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Soybean Plant ◽  
Climate Data

The plant density of soybean is a critical factor affecting plant canopy structure and yield. Predicting the spatial variability of plant density would be valuable for improving agronomic practices. The objective of this study was to develop a model for plant density measurement using several data sets with different spatial resolutions, including unmanned aerial vehicle (UAV) imagery, PlanetScope satellite imagery, and climate data. The model establishment process includes (1) performing the high-throughput measurement of actual plant density from UAV imagery with the You Only Look Once version 3 (YOLOv3) object detection algorithm, which was further treated as a response variable of the estimation models in the next step, and (2) developing regression models to estimate plant density in the extended areas using various combinations of predictors derived from PlanetScope imagery and climate data. Our results showed that the YOLOv3 model can accurately measure actual soybean plant density from UAV imagery data with a root mean square error (RMSE) value of 0.96 plants m−2. Furthermore, the two regression models, partial least squares and random forest (RF), successfully expanded the plant density prediction areas with RMSE values ranging from 1.78 to 3.67 plant m−2. Model improvement was conducted using the variable importance feature in RF, which improved prediction accuracy with an RMSE value of 1.72 plant m−2. These results demonstrated that the established model had an acceptable prediction accuracy for estimating plant density. Although the model could not often evaluate the within-field spatial variability of soybean plant density, the predicted values were sufficient for informing the field-specific status.

scholarly journals Assessing the Response of Snow Avalanche Runout Altitudes to Climate Fluctuations Using Hierarchical Modeling: Application to 61 Winters of Data in France

2010 ◽  
Vol 23 (12) ◽  
pp. 3157-3180 ◽  
Author(s):  
N. Eckert ◽  
H. Baya ◽  
M. Deschatres
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Hierarchical Modeling ◽  
Control Measures ◽  
Level Shift ◽  
Snow Avalanches ◽  
Winter Climate ◽  
Climate Fluctuations ◽  
High Magnitude ◽  
Proposed Model

Abstract Snow avalanches are natural hazards strongly controlled by the mountain winter climate, but their recent response to climate change has thus far been poorly documented. In this paper, hierarchical modeling is used to obtain robust indexes of the annual fluctuations of runout altitudes. The proposed model includes a possible level shift, and distinguishes common large-scale signals in both mean- and high-magnitude events from the interannual variability. Application to the data available in France over the last 61 winters shows that the mean runout altitude is not different now than it was 60 yr ago, but that snow avalanches have been retreating since 1977. This trend is of particular note for high-magnitude events, which have seen their probability rates halved, a crucial result in terms of hazard assessment. Avalanche control measures, observation errors, and model limitations are insufficient explanations for these trends. On the other hand, strong similarities in the pattern of behavior of the proposed runout indexes and several climate datasets are shown, as well as a consistent evolution of the preferred flow regime. The proposed runout indexes may therefore be usable as indicators of climate change at high altitudes.

Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project

2021 ◽  
Author(s):  
Erik Engström ◽  
Cesar Azorin-Molina ◽  
Lennart Wern ◽  
Sverker Hellström ◽  
Christophe Sturm ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Global Climate ◽  
Current Status ◽  
Weather Data ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Staff Members ◽  
The 1960S

<p>Here we present the progress of the first work package (WP1) of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden” (WINDGUST), funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the “stilling” phenomenon and the recent “recovery” since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).</p><p>The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.</p><p>During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.</p>

scholarly journals Atmospheric Oscillations and Related Synoptic Patterns *

1954 ◽  
Vol 35 (7) ◽  
pp. 301-309 ◽  
Author(s):  
William Donn ◽  
Richard Rommer ◽  
Frank Press ◽  
Maurice Ewing
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Weather Data ◽  
Columbia University ◽  
Air Masses ◽  
Cold Fronts ◽  
Squall Lines ◽  
Short Period ◽  
Local Weather

Records from sensitive microbarovariographs installed at Palisades, N. Y., Columbia University in New York City, U. S. Merchant Marine Academy, Kings Point, L. I. have been studied in connection with synoptic and local weather data. A number of interesting pressure events have been noted in connection with the passage of certain synoptic situations, These include pressure pump lines, squall lines, cold fronts and thunderstorms. Low level turbulence or convection associated with certain air masses at certain times is well-recorded by short-period pressure variations. Conclusions regarding the origin of squall lines are drawn from the empirical evidence given.

Cereal yield forecasting combining satellite drought-based indices, regional climate and weather data using machine learning approaches in Morocco

2021 ◽  
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Riad Balaghi ◽  
Abdelhakim Amazirh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Climate ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Drought Indices ◽  
Support Vector ◽  
Learning Approaches ◽  
Climate Data ◽  
Yield Forecasting

<p>Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R² = 0.90 and RMSE about 3.4 Qt.ha<sup>-1</sup>.  When comparing the model’s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.</p>

Explaining recent trends in extreme precipitation in the Southwestern Alps by changes in atmospheric influences 

2021 ◽  
Author(s):  
Juliette Blanchet ◽  
Antoine Blanc ◽  
Jean-Dominique Creutin
Keyword(s):  
Extreme Precipitation ◽  
Extreme Value Distribution ◽  
Mediterranean Area ◽  
Return Level ◽  
French Alps ◽  
Weather Patterns ◽  
Recent Trends ◽  
The Mediterranean ◽  
Increasing Trends ◽  
Southwestern Alps

<p>We analyze recent trends in extreme precipitation in the Southwestern Alps and link these trends to changes in the atmospheric influences triggering extremes. We consider a high-resolution precipitation dataset of 1x1 km2 for the period 1958-2017. A robust method of trend estimation is considered, based on nonstationary extreme value distribution and a homogeneous neighborhood approach. The results show contrasting extreme precipitation trends depending on the season. Excluding autumn, the significant trends are mostly negative in the Mediterranean area, while the French Alps show more contrasted trends, in particular in winter with significant increasing extremes in the Western and Southern French Alps and decreasing extremes in the Northern French Alps and Swiss Valais. In autumn, most of Southern France shows significant increasing trends, with up to 100% increase in the 20-year return level between 1958 and 2017, while the Northern French Alps show decreasing extremes.<br>By comparing these trends to changes in the occurrence of the dominant weather patterns triggering the extremes, we show that part of the significant changes in extremes can be explained by changes in the dominant influences, particularly in the Mediterranean influenced region. We also show that part of the trends in extremes are explained by a shift in the seasonality of maxima. </p>

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