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Blessed Weather

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  1. Hurricanes and Tropical Cyclones A wealth of archive data is available from the NOAA National Hurricane Center website. Here are the variables with an example of each chart or table and the link to the webpage following each example: Atlantic and Eastern Pacific Storms Climatology (seasonal progress by date/month 1966 - 2009) https://www.nhc.noaa.gov/climo/ Point of Storm Origin Climatology per 10 day period (1851 - 2015) https://www.nhc.noaa.gov/climo/ Typical Hurricane Storm Track Climatology per Month https://www.nhc.noaa.gov/climo/ Atlantic Basin Storm Count (Hurricanes & Sub-tropical Storms) - Graph & Data Table of Annual Numbers (1851 - 2015) Link to graph: https://www.nhc.noaa.gov/climo/ Link to data table: https://www.nhc.noaa.gov/climo/images/AtlanticStormTotalsTable.pdf Hurricane Return Periods (frequency of hurricanes expected within a 50 nautical mile range of US location based on 100 years data) https://www.nhc.noaa.gov/climo/ CONUS Hurricane strikes (1950 - 2017) https://www.nhc.noaa.gov/climo/ Hurricane Strike Density per County (1900 - 2010) https://www.nhc.noaa.gov/climo/ Atlantic and Eastern Pacific Archived NHC Storm Reports (per storm 1995 - 2018) https://www.nhc.noaa.gov/data/tcr/index.php?season=1995&basin=atl
  2. Atlantic Multidecadal Oscillation (AMO) The AMO is an ongoing series of long duration changes in the sea surface temperature of the North Atlantic Ocean, with cool and warm phases that may last for 20-40 years at a time and a difference of about 1°F between extremes. These changes are natural and have been occurring for at least the last 1,000 years. The AMO has affected air temperatures and rainfall over much of the Northern Hemisphere, in particular, North America and Europe. It is associated with changes in the frequency of North American droughts and is reflected in the frequency of severe Atlantic hurricanes. (Source: NOAA) Several sources of data are available in graphical or monthly values from 1856 to date. Here is an example of both the graphical output available from the multi-organisational collaborative website "The State of the Ocean Climate" (involving NOAA) and the monthly data values available in tabular format from the NOAA/ERSL/PSD website: Link to graphical output: https://stateoftheocean.osmc.noaa.gov/atm/amo.php Link to tabular data output: https://www.esrl.noaa.gov/psd/data/timeseries/AMO/
  3. Pacific Decadal Oscillation (PDO) The Pacific Decadal Oscillation (PDO) is often described as a long-lived El Niño-like pattern of Pacific climate variability (Zhang et al. 1997). As seen with the better-known El Niño/Southern Oscillation (ENSO), extremes in the PDO pattern are marked by widespread variations in the Pacific Basin and the North American climate. In parallel with the ENSO phenomenon, the extreme phases of the PDO have been classified as being either warm or cool, as defined by ocean temperature anomalies in the northeast and tropical Pacific Ocean. When SSTs are anomalously cool in the interior North Pacific and warm along the Pacific Coast, and when sea level pressures are below average over the North Pacific, the PDO has a positive value. When the climate anomaly patterns are reversed, with warm SST anomalies in the interior and cool SST anomalies along the North American coast, or above average sea level pressures over the North Pacific, the PDO has a negative value. (Description courtesy of NOAA and Mantua 1999). Several sources of data are available in graphical or monthly values from 1854 to date. Here is an example of both the graphical and tabular output available from the Joint Institute for the Study of Atmosphere and Ocean, a research collaboration between the University of Washington (UW) and the National Oceanic and Atmospheric Administration (NOAA): Link to graphical output: http://research.jisao.washington.edu/pdo/graphics.html Link to tabular data values: http://research.jisao.washington.edu/pdo/PDO.latest.txt Link to NOAA graphics and data: https://www.ncdc.noaa.gov/teleconnections/pdo/
  4. Southern Oscillation Index (SOI) The Southern Oscillation Index (SOI) is a standardized index based on the observed sea level pressure differences between Tahiti and Darwin, Australia. The SOI is one measure of the large-scale fluctuations in air pressure occurring between the western and eastern tropical Pacific (i.e., the state of the Southern Oscillation) during El Niño and La Niña episodes. Negative values coincide with El Nino events and postive values with La Nina. Data is available in graphical or monthly values from 1951 to date. Here is an example of both the graphical and tabular output: Link to graphical output: https://www.ncdc.noaa.gov/teleconnections/enso/indicators/soi/ Link to tabular data values: https://www.cpc.ncep.noaa.gov/data/indices/soi
  5. STRATOSPHERE ARCHIVE CHARTS This section provides links to archived stratosphere charts: Charts available from NASA Archived charts from 1978/79 to date for 150hPa to 10hPa are available for the following variables: Heat Flux https://ozonewatch.gsfc.nasa.gov/meteorology/flux_2018_MERRA2_NH.html 45-day total eddy heat flux 45-day wave 1–3 eddy heat flux total eddy heat flux wave 1–3 eddy heat flux Example Heat Flux chart (Monthly): Example Heat Flux chart (Annual): Ozone https://ozonewatch.gsfc.nasa.gov/meteorology/ozone_2018_MERRA2_NH.html polar cap ozone ozone min and max ozone mean latitude bands Example Ozone chart (Monthly): Example Ozone chart (Annual): Potential Vorticity https://ozonewatch.gsfc.nasa.gov/meteorology/pv_2018_MERRA2_NH.html vortex area vortex edge potential vorticity maximum potential vorticity Example Potential Vorticity (Vortex Area) chart: Temperature https://ozonewatch.gsfc.nasa.gov/meteorology/temp_2018_MERRA2_NH.html minumum temperature 60°–90°N Temperature 55°–75°N Temperature Example Temperature chart (Monthly): Example Temperature chart (Annual): Zonal Wind https://ozonewatch.gsfc.nasa.gov/meteorology/wind_2018_MERRA2_NH.html 60°N zonal wind 45°–75°N zonal wind Example Zonal Wind chart (Monthly): Example Zonal Wind chart (Annual): Charts available from Hannah E Attard, Department of Atmospheric and Environmental Sciences University at Albany, SUNY Archived charts from 21st August 2014 to date are available for the following variables: 500 hPa, DT, & 10 hPa map: 500 hPa height every 100 meters (black), potential temperature on the 2 PVU surface (shaded), and the 10 hPa height every 500 meters (white). 50 hPa & 10 hPa maps: Height every 200 meters (black), temperature in °C shaded. 100 hPa map: Height every 100 meters (black), temperature in °C shaded. Example charts: http://www.atmos.albany.edu/student/hattard/archive.php Charts available from Berlin University Archived data from 1951/52 to 2012/13 is available for the following variables: Monthly average of the 30 hPa North Pole temperature in ° C Monthly means of sunspot relative number in January QBO: the phase of quasi-biennial oscillation in January and February Occurrence of a Canadian warming Occurrence of a major stratospheric warming (major mid-winter warming) Switch to summer circulation (major final warming) Particularly cold months (monthly average of the temperature is at least half a standard deviation below the long-term average). The data is presented in tabular format as follows: Table 1: Monthly average of the 30 hPa North Pole temperature in ° C. RJ: monthly means of sunspot relative number in January; QBO: the phase of quasi-biennial oscillation (determined by the average between 50 and 40 hPa (45 hPa) in January and February); TRT: gives information about the time of transition to summer circulation (early: TMar> = -51 ° C and / or TApr> = -44 ° C), the mean zonal wind in 60 ° N must be completed by 9. April to the east)). CW: marks the occurrence of Canadian warming; * marks the occurrence of a major stratospheric warming (major mid-winter warming); * FW marks a switch to summer circulation that coincides with a major stratospheric warming (major final warming); C indicates a particularly cold month (monthly average of the temperature is at least half a standard deviation below the long-term average). After the winter of 1984/85, the long-term mean and the standard deviation are given for the first 30 years. At the bottom of the table are: the long-term mean, the standard deviation, the linear trend in K / decade for the entire time series (n = 58) and the significance in%. Updated by Labitzke and Naujokat (2000). Data: 1955/56 - 1956/57 Muench and Borden (1962), 1957/58 - 2000/01 FUB analyzes, 2001/02 - 2012/13 ECMW operational analyzes. https://www.geo.fu-berlin.de/met/ag/strat/produkte/northpole/index.html Charts available from the National Weather Service/Climate Prediction Center Archived charts from 1979 to date are available for the Southern & Northern Hemisphere and Equator for the zonal mean time series of the following variables: Temperature and Temperature Anomaly Example charts: Zonal Wind and Zonal Wind Anomaly Example charts: Normalized Geopotential Height Anomalies Example chart: Amplitude of the height field's Wave 1, Wave 2, and Wave 3. Example chart for Wave 1: https://www.cpc.ncep.noaa.gov/products/stratosphere/strat-trop/ Archived charts from 1979 to date are available for the Global Temperature Time Series for the following latitudes and levels of the stratosphere (note the links in the following illustration do not work. Please visit the NWS/CPC website - link below): https://www.cpc.ncep.noaa.gov/products/stratosphere/temperature/ EDIT: The charts and links below have been added by David @Bring Back 1962-63 Please note that we also have a specialist "QBO" page too (please refer to the index in the opening post to this library for a link). Charts from the Tokyo Climate Center: They are part of the WMO (World Meteorological Organisation) regional network. This is their Strat and QBO service. My thanks go to Tom @Isotherm for the link below). http://ds.data.jma.go.jp/tcc/tcc/products/clisys/STRAT/ Click on their drop down menu for many chart options. Two examples below (should auto update): Note that this link and these charts is also added to the QBO page as it belongs there too. Other charts and QBO data only appear there. More WMO regional data will be added to the library in due course.
  6. The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations Authors: D. N. Walters, M. J. Best, A. C. Bushell, D. Copsey, J. M. Edwards, P. D. Falloon, C. M. Harris, A. P. Lock,J. C. Manners, C. J. Morcrette, M. J. Roberts, R. A. Stratton, S. Webster, J. M. Wilkinson, M. R. Willett, I. A. Boutle,P. D. Earnshaw, P. G. Hill, C. MacLachlan, G. M. Martin, W. Moufouma-Okia, M. D. Palmer, J. C. Petch,G. G. Rooney, A. A. Scaife, and K. D. Williams Published: October 2011 Abstract: We describe Global Atmosphere 3.0 (GA3.0): a configuration of the Met Office Unified Model (MetUM) developed for use across climate research and weather prediction activities. GA3.0 has been formulated by converging the development paths of the Met Office’s weather and climate global atmospheric model components such that wherever possible, atmospheric processes are modelled or parametrized seamlessly across spatial resolutions and timescales. This unified development process will provide the Met Office and its collaborators with regular releases of a configuration that has been evaluated, and can hence be applied, over a variety of modelling regimes. We also describe Global Land 3.0 (GL3.0): a configuration of the JULES community land surface model developed for use with GA3.0. This paper provides a comprehensive technical and scientific description of the GA3.0 and GL3.0 (and related GA3.1and GL3.1) configurations and presents the results of some initial evaluations of their performance in various applications. It is to be the first in a series of papers describing each subsequent Global Atmosphere release; this will provide a single source of reference for established users and developers as well as researchers requiring access to a current, but trusted, global MetUM setup. Link to full paper: https://www.geosci-model-dev.net/4/919/2011/gmd-4-919-2011.pdf
  7. Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system Authors: J. Camp M. Roberts C. MacLachlan E. Wallace L. Hermanson A. Brookshaw A. Arribas A. A. Scaife Published: January 2015 Abstract: The variability and predictability of tropical storm activity in the Met Office fully coupled atmosphere–ocean Global Seasonal Forecast System 5 (GloSea5) is assessed. GloSea5 is a high‐resolution seasonal forecast system with an atmospheric horizontal grid of 0.83° longitude × 0.55° latitude (∼53 km at 55°N) and 0.25° in the global ocean. The performance of the system is assessed in terms of its ability to retrospectively predict the observed tropical storm climatology and its response to the El Niño–Southern Oscillation (ENSO). Results are compared to the predecessor system GloSea4 (∼120 km atmospheric horizontal resolution) and observational analyses over the common period of the operational hindcast for both systems: 1996–2009. A supplementary assessment of GloSea5 for the period 1992–2013 is then performed to evaluate skill of tropical storm predictions in the Northern Hemisphere as well as landfall frequency along two regions, the US coast and the Caribbean, over a longer period. GloSea5 is able to reproduce key tropical storm characteristics, such as their geographical distribution, seasonal cycle and interannual variability, as well as spatial changes in storm track density with ENSO. GloSea5 shows statistically significant skill for predictions of tropical storm numbers and accumulated cyclone energy (ACE) index in the North Atlantic, western Pacific, Australian region and South Pacific. Statistically significant skill is also found for predictions of landfall frequency along the Caribbean coastline. Skill is similar using either the direct counting of landfalling storms in the model, or by inferring landfall rates from the Atlantic basin‐wide storm count. We find no skill for predictions of landfall along the US coast. Results suggest the potential for operational seasonal tropical storm forecasts throughout the Tropics. Link to full paper: https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.2516
  8. The NCEP climate forecast system version 2 Authors: Suranjana Saha, Sundar Moorthi, Xingren Wu, Jiande Wang Published: March 2014 Abstract: The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979-2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982-2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season. Link to full paper: https://www.researchgate.net/publication/273200037_The_NCEP_climate_forecast_system_version_2
  9. The quiet revolution of numerical weather prediction Authors: Peter Bauer, Alan Thorpe, Gilbert Brunet Published: Sept 2015 Abstract: Advances in numerical weather prediction represent a quiet revolution because they have resulted from a steady accumulation of scientific knowledge and technological advances over many years that, with only a few exceptions, have not been associated with the aura of fundamental physics breakthroughs. Nonetheless, the impact of numerical weather prediction is among the greatest of any area of physical science. As a computational problem, global weather prediction is comparable to the simulation of the human brain and of the evolution of the early Universe, and it is performed every day at major operational centres across the world. Link to full paper: https://www.researchgate.net/publication/281516336_The_quiet_revolution_of_numerical_weather_prediction
  10. Impact of Atmosphere and Land Surface Initial Conditions on Seasonal Forecasts of Global Surface Temperature Authors: Stefano Materia, Andrea Borrelli, Alessio Bellucci, and Andrea Alessandri Published: Dec 2014 Abstract: The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, increasing the model predictive skill in the ocean. In fact, in regions characterized by strong air–sea coupling, the atmosphere initial condition affects forecast skill for several months. In particular, the ENSO region, eastern tropical Atlantic, and North Pacific benefit significantly from the atmosphere initialization. On the mainland, the effect of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects forecast skill in the following seasons. Winter forecasts in the high-latitude plains benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region and central Asia. However, the initialization strategy based on the full value technique may not be the best choice for land surface, since soil moisture is a strongly model-dependent variable: in fact, initialization through land surface reanalysis does not systematically guarantee a skill improvement. Nonetheless, using a different initialization strategy for land, as opposed to atmosphere and ocean, may generate inconsistencies. Overall, the introduction of a realistic initialization for land and atmosphere substantially increases skill and accuracy. However, further developments in the procedure for land surface initialization are required for more accurate seasonal forecasts. Link to full paper: https://journals.ametsoc.org/doi/10.1175/JCLI-D-14-00163.1
  11. The HadGEM2 family of Met Office Unified Model Climate configurations Authors: The HadGEM2 Development Team: G. M. Martin, N. Bellouin, William J Collins, I. D. Culverwell. Published: Sept 2011 Abstract: We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability. Link to full paper: https://www.researchgate.net/publication/230706683_The_HadGEM2_family_of_Met_Office_Unified_Model_Climate_configurations
  12. Statement of Guidance for Global Numerical Weather Prediction (NWP) Authors: Erik Andersson, ECMWF Published: May 2018 Abstract: No abstract, but here are some extracts: Global Numerical Weather Prediction (NWP) models are used to produce short- and medium-range weather forecasts (out to 10-15 days) of the state of the atmosphere, with a horizontal resolution of typically 10-25 km and a vertical resolution of 10-30 m near the surface increasing to 500 m-1 km in the stratosphere. Ensembles of up to 50 members of such forecasts provide estimates of uncertainty. Recent developments on coupled forecasting systems indicate the benefits of coupling ocean and sea ice models with the atmosphere for the NWP forecasts. The timely initialization of the sea-ice and ocean therefore needs to be considered. Nowadays the same coupled atmosphere-land-wave-seaice-ocean model is used for the medium and extended range (30-60 days) forecasts. The following sections provide an assessment, for the main variables of interest, of how well the observational requirements are met by existing or planned observing systems. Link to full paper: https://www.wmo.int/pages/prog/www/OSY/SOG/SoG-Global-NWP.pdf
  13. Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system Authors: H. T. Hewitt, D. Copsey, I. D. Culverwell, C. M. Harris, R. S. R. Hill, A. B. Keen, A. J. McLaren, and E. C. Hunke. (Met Office, Hadley Centre, Exeter, Devon, UK). Published: April 2011 Abstract: This paper describes the development of a technically robust climate modelling system, HadGEM3, which couples the Met Office Unified Model atmosphere component, the NEMO ocean model and the Los Alamos sea-ice model (CICE) using the OASIS coupler. Details of the coupling and technical solutions of the physical model (HadGEM3-AO) are documented, in addition to a description of the configurations of the individual submodels. The paper demonstrates that the implementation of the model has resulted in accurate conservation of heat and freshwater across the model components. The model performance in early versions of this climate model is briefly described to demonstrate that the results are scientifically credible. HadGEM3-AO is the basis for a number of modelling efforts outside of the Met Office, both within the UK and internationally. This documentation of the HadGEM3-AO system provides a detailed reference for developers of HadGEM3-based climate configurations. Link to full paper: https://www.geosci-model-dev.net/4/223/2011/gmd-4-223-2011.pdf
  14. The challenge of seasonal weather prediction Author: Emma Critchley, Imperial College London. Published: April 2014 Abstract: No abstract, but here's a few snippets: In April 2009 the UK Met Office issued their now infamous forecast: “odds-on for a BBQ summer”. By the end of August, total precipitation since June had climbed to 42% above average levels for 1971-2000. Why is it so challenging to provide seasonal forecasts several months ahead? As we move from forecasting days ahead, to weeks, months, seasons and decades, the number and complexity of physical processes that must be well described by the computer models used to simulate the weather increases. Delivering better forecasts requires improvements on both these fronts, which compete for limited computer resources. Link to full paper: https://wwwf.imperial.ac.uk/blog/climate-at-imperial/2014/04/14/the-challenge-of-seasonal-weather-prediction/
  15. Global Seasonal forecast system version 5 (GloSea5): a high‐resolution seasonal forecast system Authors: C. MacLachlan, A. Arribas, K. A. Peterson, A. Maidens, D. Fereday, A. A. Scaife, M. Gordon, M. Vellinga, A. Williams, R. E. Comer, J. Camp, P. Xavier, G. Madec. Published: May 2014 Abstract: This article describes the UK Met Office Global Seasonal forecast system version 5 (GloSea5). GloSea5 upgrades include an increase in horizontal resolution in the atmosphere (N216–0.7°) and the ocean (0.25°), and implementation of a 3D‐Var assimilation system for ocean and sea‐ice conditions. GloSea5 shows improved year‐to‐year predictions of the major modes of variability. In the Tropics, predictions of the El Niño–Southern Oscillation are improved with reduced errors in the West Pacific. In the Extratropics, GloSea5 shows unprecedented levels of forecast skill and reliability for both the North Atlantic Oscillation and the Arctic Oscillation. We also find useful levels of skill for the western North Pacific Subtropical High which largely determines summer precipitation over East Asia. Link to full paper: https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.2396
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