The European Centre for Medium-Range Weather Forecasts (ECMWF) is an independent intergovernmental organisation supported by most of the nations of Europe. It is based at three sites: Shinfield Park, Reading, United Kingdom; Bologna, Italy; and Bonn, Germany. It operates one of the largest supercomputer complexes in Europe and the world's largest archive of numerical weather prediction data.
Value at risk (VaR) is a measure of the risk of loss of investment/Capital. It estimates how much a set of investments might lose, given normal market conditions, in a set time period such as a day. VaR is typically used by firms and regulators in the financial industry to gauge the amount of assets needed to cover possible losses.
A general circulation model (GCM) is a type of climate model. It employs a mathematical model of the general circulation of a planetary atmosphere or ocean. It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources. These equations are the basis for computer programs used to simulate the Earth's atmosphere or oceans. Atmospheric and oceanic GCMs are key components along with sea ice and land-surface components.
Forecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term. Forecasting might refer to specific formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods or the process of prediction and resolution itself. Usage can vary between areas of application: for example, in hydrology the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.
Jule Gregory Charney was an American meteorologist who played an important role in developing numerical weather prediction and increasing understanding of the general circulation of the atmosphere by devising a series of increasingly sophisticated mathematical models of the atmosphere. His work was the driving force behind many national and international weather initiatives and programs.
Numerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs.
Retrodiction is the act of making a prediction about the past. It is also known as postdiction.
The ECMWF reanalysis project is a meteorological reanalysis project carried out by the European Centre for Medium-Range Weather Forecasts (ECMWF). The first reanalysis product, ERA-15, generated reanalyses for approximately 15 years, from December 1978 to February 1994. The second product, ERA-40 begins in 1957 and covers 45 years to 2002. As a precursor to a revised extended reanalysis product to replace ERA-40, ECMWF released ERA-Interim, which covers the period from 1979 to 2019. A new reanalysis product ERA5 has recently been released by ECMWF as part of Copernicus Climate Change Services. This product has higher spatial resolution and covers the period from 1979 to present. Extension up to 1940 became available in 2023.
Ensemble forecasting is a method used in or within numerical weather prediction. Instead of making a single forecast of the most likely weather, a set of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere. Ensemble forecasting is a form of Monte Carlo analysis. The multiple simulations are conducted to account for the two usual sources of uncertainty in forecast models: (1) the errors introduced by the use of imperfect initial conditions, amplified by the chaotic nature of the evolution equations of the atmosphere, which is often referred to as sensitive dependence on initial conditions; and (2) errors introduced because of imperfections in the model formulation, such as the approximate mathematical methods to solve the equations. Ideally, the verified future atmospheric state should fall within the predicted ensemble spread, and the amount of spread should be related to the uncertainty (error) of the forecast. In general, this approach can be used to make probabilistic forecasts of any dynamical system, and not just for weather prediction.
Data assimilation is a mathematical discipline that seeks to optimally combine theory with observations. There may be a number of different goals sought – for example, to determine the optimal state estimate of a system, to determine initial conditions for a numerical forecast model, to interpolate sparse observation data using knowledge of the system being observed, to set numerical parameters based on training a model from observed data. Depending on the goal, different solution methods may be used. Data assimilation is distinguished from other forms of machine learning, image analysis, and statistical methods in that it utilizes a dynamical model of the system being analyzed.
The Atlantic hurricane reanalysis project of the National Oceanic and Atmospheric Administration seeks to correct and add new information about past North Atlantic hurricanes. It was started around 2000 to update HURDAT, the official hurricane database for the Atlantic Basin, which has become outdated since its creation due to various systematic errors introduced into the database over time. This effort has involved reanalyses of ship observations from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) as well as reanalyses done by other researchers over the years. It has been ongoing as of 2024.
The Norwegian Meteorological Institute, also known internationally as MET Norway, is Norway's national meteorological institute. It provides weather forecasts for civilian and military uses and conducts research in meteorology, oceanography and climatology. It is headquartered in Oslo and has offices and stations in other cities and places. It has around 500 full-time staff and was founded in 1866.
The NCEP/NCAR Reanalysis is an atmospheric reanalysis produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). It is a continually updated globally gridded data set that represents the state of the Earth's atmosphere, incorporating observations and numerical weather prediction (NWP) model output from 1948 to present.
Jagadish Shukla is an Indian meteorologist and Distinguished University Professor at George Mason University in the United States.
In fluid dynamics, wind wave modeling describes the effort to depict the sea state and predict the evolution of the energy of wind waves using numerical techniques. These simulations consider atmospheric wind forcing, nonlinear wave interactions, and frictional dissipation, and they output statistics describing wave heights, periods, and propagation directions for regional seas or global oceans. Such wave hindcasts and wave forecasts are extremely important for commercial interests on the high seas. For example, the shipping industry requires guidance for operational planning and tactical seakeeping purposes.
In finance, model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial securities.
An atmospheric reanalysis is a meteorological and climate data assimilation project which aims to assimilate historical atmospheric observational data spanning an extended period, using a single consistent assimilation scheme throughout.
Walk forward optimization is a method used in finance to determine the optimal parameters for a trading strategy and to determine the robustness of the strategy. Walk Forward Analysis was created by Robert E. Pardo in 1992 and expanded in the second edition. Walk Forward Analysis is now widely considered the "gold standard" in trading strategy validation. The trading strategy is optimized with in-sample data for a time window in a data series. The remaining data is reserved for out of sample testing. A small portion of the reserved data following the in-sample data is tested and the results are recorded. The in-sample time window is shifted forward by the period covered by the out of sample test, and the process repeated. Lastly, all of the recorded results are used to assess the trading strategy.
Masao Kanamitsu was a Japanese and American atmospheric scientist working in the field of data assimilation. His research greatly influenced global and regional climate change studies including development of breakthrough reanalysis and downscaling datasets and weather forecasting studies. He was the co-author of one of the most cited geophysics paper in his time.
The Simple Ocean Data Assimilation (SODA) analysis is an oceanic reanalysis data set consisting of gridded state variables for the global ocean, as well as several derived fields. SODA was developed in the 1990s as a collaborative project between the Department of Atmospheric and Oceanic Science at the University of Maryland and the Department of Oceanography at Texas A&M University with the goal of providing an improved estimate of ocean state from those based solely on observations or numerical simulations. Since its first release there have been several updates, the most recent of which extends from 1958 to 2008, as well as a “beta release” of a long-term reanalysis for 1871–2008.
