Assimilating Cloud Optical Depth for Applications to Extreme Weather Events

2021 ◽  
Author(s):  
JUXIANG PENG ◽  
YUANFU XIE ◽  
ZHAOPING KANG
Keyword(s):  
Relative Humidity ◽  
Optical Depth ◽  
The United States ◽  
Extreme Weather ◽  
Cloud Water ◽  
Extreme Weather Events ◽  
Cloud Optical Depth ◽  
Weather Events ◽  
Analysis System ◽  
Cloud Ice

AbstractThis paper reports the assimilation of cloud optical depth datasets into a variational data assimilation system to improve cloud ice, cloud water, rain, snow, and graupel analysis in extreme weather events for improving forecasts. A cloud optical depth forward operator was developed and implemented in the Space and Time Multiscale Analysis System (STMAS), a multiscale three-dimensional variational analysis system. Using this improved analysis system, the NOAA GOES-15 DCOMP (Daytime Cloud Optical and Microphysical Properties) cloud optical depth products were assimilated to improve the microphysical states. For an eight-day period of extreme weather events in September 2013 in Colorado, the United States, the impact of the cloud optical depth assimilation on the analysis results and forecasts was evaluated. The DCOMP products improved the cloud ice and cloud water predictions significantly in convective and lower levels. The DCOMP products also reduced errors in temperature and relative humidity data at the top (250–150 hPa) and bottom (850–700 hPa) layers. With the cloud ice improvement at higher layers, the DCOMP products provided better forecasts of cloud liquid at low layers (900–700 hPa), temperature and wind at all layers, and relative humidity at middle and bottom layers. Furthermore, for this extreme weather event, both equitable threat score (ETS) and bias were improved throughout the 12 h period, with the most significant improvement observed in the first 3 h. This study will raise the expectation of cloud optical depth product assimilation in operational applications.

The relationship between extreme weather and low crop yields

2020 ◽  
Author(s):  
Matias Heino ◽  
Weston Anderson ◽  
Michael Puma ◽  
Matti Kummu
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Crop Yields ◽  
Explanatory Power ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Yield Variability ◽  
Weather Events

<p>It is well known that climate extremes and variability have strong implications for crop productivity. Previous research has estimated that annual weather conditions explain a third of global crop yield variability, with explanatory power above 50% in several important crop producing regions. Further, compared to average conditions, extreme events contribute a major fraction of weather induced crop yield variations. Here we aim to analyse how extreme weather events are related to the likelihood of very low crop yields at the global scale. We investigate not only the impacts of heat and drought on crop yields but also excess soil moisture and abnormally cool temperatures, as these extremes can be detrimental to crops as well. In this study, we combine reanalysis weather data with national and sub-national crop production statistics and assess relationships using statistical copulas methods, which are especially suitable for analysing extremes. Further, because irrigation can decrease crop yield variability, we assess how the observed signals differ in irrigated and rainfed cropping systems. We also analyse whether the strength of the observed statistical relationships could be explained by socio-economic factors, such as GDP, social stability, and poverty rates. Our preliminary results indicate that extreme heat and cold as well as soil moisture abundance and excess have a noticeable effect on crop yields in many areas around the globe, including several global bread baskets such as the United States and Australia. This study will increase understanding of extreme weather-related implications on global food production, which is relevant also in the context of climate change, as the frequency of extreme weather events is likely to increase in many regions worldwide.</p>

Extreme Weather and Climate

2017 ◽  
Author(s):  
Friederike Otto
Keyword(s):  
Extreme Events ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Event ◽  
Weather Forecasts ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Weather Events ◽  
Global Mean

Natural disasters and extreme weather events have been of great societal importance throughout history and often brought everyday life to a catastrophic halt, in a way sometimes comparable to wars and epidemics, only without the lead time. Extreme weather events with large impacts serve as an anchor point of the collective memory of the population in the affected area. Every northern German of the right age remembers the storm surge of 1962 and where they were at the time and has friends or family effected by the event. The “dust bowl” of the 1930s with extensive droughts and heat waves shaped the life of a generation in the United States, and the Sahel droughts in the 1960s and 1970s led to famine and dislocation of population on a massive scale the region arguably never quite recovered from. Hurricane Hyian in 2013 is said to have directly influenced the outcome of the annual Conference of the Parties (COP) United Nation Framework Convention for Climate Change Negotiations in Warsaw, leading to the inclusion of a mechanism to deal with loss and damage from climate-related disasters. Though earthquakes are still fairly unpredictable on short timescales, this is not the case for weather events. Weather forecasts today are so good that we normally know the time and location of the landfall of a hurricane within a 100-mile radius days in advance. Improvements in the prediction of slow-onset events such as droughts (which depend on the rainfall over a large region and whole season) are less striking but have still improved dramatically in the late 20th and early 21st centuries. One of the major reasons for the large increase in the accuracy of weather forecasts is the exponential increase in computing power, which allows scientists to predict and study extreme weather events using complex computer models, simulating possible weather events under certain conditions to understand the statistics of and physical mechanisms behind extreme events. Extreme events are by definition rare and thus impossible to understand from historical records of weather observation alone. Despite the progress on our understanding of and ability to predict extreme weather events, substantial uncertainties remain. Two aspects are of particular importance. Firstly, we know that the climate is changing, having observed almost a one-degree increase in global mean temperature. However, global mean temperature doesn’t kill anyone, extreme weather events do. Their frequency and intensity is changing and will continue to change, but the extent of these changes depends on a host of both global and local factors. Secondly, whether or not a rare weather event leads to extreme impacts depends largely on the vulnerability and exposure of the affected societies. If these are high, even a perfectly forecasted weather event leads to disaster.

scholarly journals Income-based Inequality in Post-disaster Migration is Lower in High Resilience Areas: Evidence from U.S. Internal Migration

2021 ◽  
Author(s):  
Ted Hsuan Yun Chen ◽  
Boyoon Lee
Keyword(s):  
Climate Change ◽  
Internal Migration ◽  
Local Level ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Disaster Resilience ◽  
Migration Patterns ◽  
Weather Events ◽  
Post Disaster

Residential relocation following extreme weather events is among the costliest individual-level measures of climate change adaptation. Consequently, they are fraught with inequalities, with disadvantaged groups most adversely impacted. As climate change continues to exacerbate extreme weather events, it is imperative that we better understand how existing socioeconomic inequalities affect climate migration and how they may be offset. In this study we use network regression models to look at how internal migration patterns in the United States vary by disaster-related property damage, household income, and local-level disaster resilience. Our results show that post-disaster migration patterns vary considerably by the income level of sending and receiving counties, which suggests that income-based inequality impacts both access to relocation for individuals and the ability to rebuild for disaster-afflicted areas. We further find evidence that these inequalities are attenuated in areas with higher disaster resilience. However, because existing resilience incentivizes in situ incremental adaptation which can be a long term drain on individual wellbeing and climate adaptation resources, they should be balanced with policies that encourage relocation where appropriate.

Extreme weather events will hit insurance industry

2015 ◽  
Keyword(s):  
Climate Change ◽  
United States ◽  
Insurance Industry ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Causal Connection ◽  
Economic Implications ◽  
Content Type ◽  
Weather Events

Subject The political and economic implications of greater scientific understanding of extreme weather events. Significance Preparatory talks for the UN climate summit in Paris have seen representatives from developing countries ask the United States and EU for greater compensation for damages caused by extreme weather. The link between climate change and more extreme weather events is clear -- energy from higher temperature levels can be translated into kinetic energy and disrupts usual weather patterns -- but distinguishing the extent of a causal connection, especially for specific events, has until recently been difficult. Impacts Extreme weather events will affect the insurance industry, agriculture, tourism, and food and beverage sectors. In the United States, the South-east will see the highest risks of coastal property losses due to climate change impacts. Hurricanes and other coastal storms combined with rising sea levels are likely to cause growing annual storm losses in the Caribbean. Infrastructure will grow in cost as it must be proofed against new extremes in weather stress.

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