Emancipate Yourself from Mental SlaveryShawn Collins
Shawn Collins, Co-Founder of the Affiliate Summit conference and tradeshow, shares two devastating stories that became catalysts for him to go out on his own as an entrepreneur.
The document describes a get a $500 Nordstrom gift card by following several steps: clicking on a link from a website, clicking on a banner on that page, entering an email address, and that's all that is required to participate. It mentions the gift card can be received by residents of the USA by following these simple steps as outlined on the website provided.
1) The document analyzes seasonal forecasts for global solar PV energy availability in autumn.
2) It identifies several key regions where solar GHI is both abundant and highly variable in autumn, including Spain/Portugal, Indonesia, eastern Australia, and Tanzania/Kenya Coast.
3) It assesses the skill of climate forecast models in predicting autumn solar GHI variability and magnitude up to 1 month in advance, finding the highest skill in regions like Spain/Portugal, Indonesia, northeast USA/Caribbean, and northeast Australia.
1) The document analyzes seasonal forecasts for global solar PV energy potential over summer (June, July, August).
2) It identifies several areas of highest interest based on regions that have both abundant and highly variable solar radiation resources as well as regions where climate forecast models demonstrate the highest skill in predicting summer solar variability.
3) An example is shown of an operational seasonal forecast from May 2011 predicting the probability of above, below, or normal solar radiation for the forthcoming summer in areas that were identified as highest priority.
Omaxe Service Apartments Mullanpur New Chandigarh Puneet VaidFuture Estates
Omaxe is launching service apartments in Mullanpur, the new extension of Chandigarh. The apartments will be located in Omaxe's five-star Hotel Holiday Inn Service Apartments. There will be approximately 200 service apartments available in a G+22 high-rise building. The apartments range in size from 480 square feet to 1440 square feet and come fully furnished with amenities like a swimming pool, club, gym and restaurant. Buyers can choose from payment plans including a down payment plan with 12% annual assured returns or construction-linked plans. The service apartments are a viable investment due to the development of a medical city, film city, educational hub and commercial areas near the location in Mullanpur
Baseball pants worn by players these days score high in terms of comfort as well as style. Piping and graphics are more common on modern baseball pants
Emancipate Yourself from Mental SlaveryShawn Collins
Shawn Collins, Co-Founder of the Affiliate Summit conference and tradeshow, shares two devastating stories that became catalysts for him to go out on his own as an entrepreneur.
The document describes a get a $500 Nordstrom gift card by following several steps: clicking on a link from a website, clicking on a banner on that page, entering an email address, and that's all that is required to participate. It mentions the gift card can be received by residents of the USA by following these simple steps as outlined on the website provided.
1) The document analyzes seasonal forecasts for global solar PV energy availability in autumn.
2) It identifies several key regions where solar GHI is both abundant and highly variable in autumn, including Spain/Portugal, Indonesia, eastern Australia, and Tanzania/Kenya Coast.
3) It assesses the skill of climate forecast models in predicting autumn solar GHI variability and magnitude up to 1 month in advance, finding the highest skill in regions like Spain/Portugal, Indonesia, northeast USA/Caribbean, and northeast Australia.
1) The document analyzes seasonal forecasts for global solar PV energy potential over summer (June, July, August).
2) It identifies several areas of highest interest based on regions that have both abundant and highly variable solar radiation resources as well as regions where climate forecast models demonstrate the highest skill in predicting summer solar variability.
3) An example is shown of an operational seasonal forecast from May 2011 predicting the probability of above, below, or normal solar radiation for the forthcoming summer in areas that were identified as highest priority.
Omaxe Service Apartments Mullanpur New Chandigarh Puneet VaidFuture Estates
Omaxe is launching service apartments in Mullanpur, the new extension of Chandigarh. The apartments will be located in Omaxe's five-star Hotel Holiday Inn Service Apartments. There will be approximately 200 service apartments available in a G+22 high-rise building. The apartments range in size from 480 square feet to 1440 square feet and come fully furnished with amenities like a swimming pool, club, gym and restaurant. Buyers can choose from payment plans including a down payment plan with 12% annual assured returns or construction-linked plans. The service apartments are a viable investment due to the development of a medical city, film city, educational hub and commercial areas near the location in Mullanpur
Baseball pants worn by players these days score high in terms of comfort as well as style. Piping and graphics are more common on modern baseball pants
This document summarizes seasonal forecasts for global solar PV energy from the Climate Forecasting Unit. It identifies regions with the highest solar resource potential and variability in spring (March-May), and assesses the skill of climate forecast models to predict spring solar radiation levels up to 1 month in advance. Several key areas are identified where solar forecasts are most skillful and could provide valuable information for decision-making, including parts of South America, Southeast Asia, Southern Africa, Northern Australia and Western Europe. An example operational forecast for spring 2011 illustrates probabilistic predictions of above-, below- or normal solar radiation levels.
1) The document analyzes seasonal forecasts for global solar photovoltaic (PV) energy in winter by assessing solar irradiance resource potential, variability, and forecast skill.
2) It identifies key regions where solar irradiance is abundant and highly variable, and where forecast models demonstrate the highest skill in predicting irradiance variability, magnitude, and uncertainty.
3) These regions, including parts of South America, Africa, Asia, and Australia, show the greatest potential for operational winter solar irradiance forecasts to inform decision-making.
1) The document provides seasonal forecasts for autumn solar photovoltaic (PV) energy potential in key regions globally based on solar irradiance data from 1981-2011.
2) It identifies regions where solar irradiance is most abundant and variable, and where seasonal forecast skill is highest one month in advance, such as Spain, East Australia, and Indonesia.
3) An example operational forecast for autumn 2011 predicts areas likely to have above, below, or normal solar irradiance that season.
1) The document examines seasonal forecasts for global wind energy during the summer, focusing on regions where wind resource is abundant and highly variable.
2) It analyzes wind resource availability and variability from 1981-2011 to identify key regions of interest, including Patagonia/Chile, Central Sahara/Sahel/Kenya, Central-Western India, Central-Southern Western Continent/Western China, and Northern Australia/Tasmania.
3) It assesses the skill of seasonal wind forecasts from 1981-2010 against observations, finding the highest skill in regions like Northeast Coast/Eastern Brasil/Northwest Coast, Southeast Continent/India, and Sahel/Western Angola/Western Namib
This document discusses seasonal forecasts for global wind energy in winter. It begins by showing maps of average winter wind resource and variability based on reanalysis data. Several regions with abundant and variable wind resources are identified. The document then assesses the skill of climate forecast models to predict winter wind variability up to 1 month in advance. Maps show where forecasts best match observations. Key regions with both high wind potential and skilled forecasts are identified. Finally, an example operational probabilistic forecast for winter 2011 wind resource is presented, focused on the most skillful regions.
1) The document analyzes seasonal forecasts for global wind energy availability in autumn.
2) It identifies several key regions where wind resource is both abundant and highly variable between years, making them most suitable for seasonal wind forecasting.
3) The forecasts are evaluated against past data and found to have the highest skill in predicting wind resource variability, magnitude, and uncertainty in certain regions like Patagonia, parts of Africa, Asia, and Australia.
1) The document analyzes seasonal forecasts for global solar PV energy, focusing on summer.
2) It identifies several key areas where solar GHI is both abundant and highly variable, making them most vulnerable to changes and important for seasonal forecasting.
3) It evaluates the skill of climate forecast models in predicting past variability and magnitude of solar GHI, finding some regions where forecasts show high skill up to 1 month ahead.
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AI Transformation Playbook: Thinking AI-First for Your BusinessArijit Dutta
I dive into how businesses can stay competitive by integrating AI into their core processes. From identifying the right approach to building collaborative teams and recognizing common pitfalls, this guide has got you covered. AI transformation is a journey, and this playbook is here to help you navigate it successfully.
Unlocking WhatsApp Marketing with HubSpot: Integrating Messaging into Your Ma...Niswey
50 million companies worldwide leverage WhatsApp as a key marketing channel. You may have considered adding it to your marketing mix, or probably already driving impressive conversions with WhatsApp.
But wait. What happens when you fully integrate your WhatsApp campaigns with HubSpot?
That's exactly what we explored in this session.
We take a look at everything that you need to know in order to deploy effective WhatsApp marketing strategies, and integrate it with your buyer journey in HubSpot. From technical requirements to innovative campaign strategies, to advanced campaign reporting - we discuss all that and more, to leverage WhatsApp for maximum impact. Check out more details about the event here https://events.hubspot.com/events/details/hubspot-new-delhi-presents-unlocking-whatsapp-marketing-with-hubspot-integrating-messaging-into-your-marketing-strategy/
Adani Group's Active Interest In Increasing Its Presence in the Cement Manufa...Adani case
Time and again, the business group has taken up new business ventures, each of which has allowed it to expand its horizons further and reach new heights. Even amidst the Adani CBI Investigation, the firm has always focused on improving its cement business.
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART INDIA MATKA KALYAN SATTA MATKA 420 INDIAN MATKA SATTA KING MATKA FIX JODI FIX FIX FIX SATTA NAMBAR MATKA INDIA SATTA BATTA
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART
This document summarizes seasonal forecasts for global solar PV energy from the Climate Forecasting Unit. It identifies regions with the highest solar resource potential and variability in spring (March-May), and assesses the skill of climate forecast models to predict spring solar radiation levels up to 1 month in advance. Several key areas are identified where solar forecasts are most skillful and could provide valuable information for decision-making, including parts of South America, Southeast Asia, Southern Africa, Northern Australia and Western Europe. An example operational forecast for spring 2011 illustrates probabilistic predictions of above-, below- or normal solar radiation levels.
1) The document analyzes seasonal forecasts for global solar photovoltaic (PV) energy in winter by assessing solar irradiance resource potential, variability, and forecast skill.
2) It identifies key regions where solar irradiance is abundant and highly variable, and where forecast models demonstrate the highest skill in predicting irradiance variability, magnitude, and uncertainty.
3) These regions, including parts of South America, Africa, Asia, and Australia, show the greatest potential for operational winter solar irradiance forecasts to inform decision-making.
1) The document provides seasonal forecasts for autumn solar photovoltaic (PV) energy potential in key regions globally based on solar irradiance data from 1981-2011.
2) It identifies regions where solar irradiance is most abundant and variable, and where seasonal forecast skill is highest one month in advance, such as Spain, East Australia, and Indonesia.
3) An example operational forecast for autumn 2011 predicts areas likely to have above, below, or normal solar irradiance that season.
1) The document examines seasonal forecasts for global wind energy during the summer, focusing on regions where wind resource is abundant and highly variable.
2) It analyzes wind resource availability and variability from 1981-2011 to identify key regions of interest, including Patagonia/Chile, Central Sahara/Sahel/Kenya, Central-Western India, Central-Southern Western Continent/Western China, and Northern Australia/Tasmania.
3) It assesses the skill of seasonal wind forecasts from 1981-2010 against observations, finding the highest skill in regions like Northeast Coast/Eastern Brasil/Northwest Coast, Southeast Continent/India, and Sahel/Western Angola/Western Namib
This document discusses seasonal forecasts for global wind energy in winter. It begins by showing maps of average winter wind resource and variability based on reanalysis data. Several regions with abundant and variable wind resources are identified. The document then assesses the skill of climate forecast models to predict winter wind variability up to 1 month in advance. Maps show where forecasts best match observations. Key regions with both high wind potential and skilled forecasts are identified. Finally, an example operational probabilistic forecast for winter 2011 wind resource is presented, focused on the most skillful regions.
1) The document analyzes seasonal forecasts for global wind energy availability in autumn.
2) It identifies several key regions where wind resource is both abundant and highly variable between years, making them most suitable for seasonal wind forecasting.
3) The forecasts are evaluated against past data and found to have the highest skill in predicting wind resource variability, magnitude, and uncertainty in certain regions like Patagonia, parts of Africa, Asia, and Australia.
1) The document analyzes seasonal forecasts for global solar PV energy, focusing on summer.
2) It identifies several key areas where solar GHI is both abundant and highly variable, making them most vulnerable to changes and important for seasonal forecasting.
3) It evaluates the skill of climate forecast models in predicting past variability and magnitude of solar GHI, finding some regions where forecasts show high skill up to 1 month ahead.
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AI Transformation Playbook: Thinking AI-First for Your BusinessArijit Dutta
I dive into how businesses can stay competitive by integrating AI into their core processes. From identifying the right approach to building collaborative teams and recognizing common pitfalls, this guide has got you covered. AI transformation is a journey, and this playbook is here to help you navigate it successfully.
Unlocking WhatsApp Marketing with HubSpot: Integrating Messaging into Your Ma...Niswey
50 million companies worldwide leverage WhatsApp as a key marketing channel. You may have considered adding it to your marketing mix, or probably already driving impressive conversions with WhatsApp.
But wait. What happens when you fully integrate your WhatsApp campaigns with HubSpot?
That's exactly what we explored in this session.
We take a look at everything that you need to know in order to deploy effective WhatsApp marketing strategies, and integrate it with your buyer journey in HubSpot. From technical requirements to innovative campaign strategies, to advanced campaign reporting - we discuss all that and more, to leverage WhatsApp for maximum impact. Check out more details about the event here https://events.hubspot.com/events/details/hubspot-new-delhi-presents-unlocking-whatsapp-marketing-with-hubspot-integrating-messaging-into-your-marketing-strategy/
Adani Group's Active Interest In Increasing Its Presence in the Cement Manufa...Adani case
Time and again, the business group has taken up new business ventures, each of which has allowed it to expand its horizons further and reach new heights. Even amidst the Adani CBI Investigation, the firm has always focused on improving its cement business.
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART INDIA MATKA KALYAN SATTA MATKA 420 INDIAN MATKA SATTA KING MATKA FIX JODI FIX FIX FIX SATTA NAMBAR MATKA INDIA SATTA BATTA
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Efficient PHP Development Solutions for Dynamic Web ApplicationsHarwinder Singh
Unlock the full potential of your web projects with our expert PHP development solutions. From robust backend systems to dynamic front-end interfaces, we deliver scalable, secure, and high-performance applications tailored to your needs. Trust our skilled team to transform your ideas into reality with custom PHP programming, ensuring seamless functionality and a superior user experience.
L'indice de performance des ports à conteneurs de l'année 2023SPATPortToamasina
Une évaluation comparable de la performance basée sur le temps d'escale des navires
L'objectif de l'ICPP est d'identifier les domaines d'amélioration qui peuvent en fin de compte bénéficier à toutes les parties concernées, des compagnies maritimes aux gouvernements nationaux en passant par les consommateurs. Il est conçu pour servir de point de référence aux principaux acteurs de l'économie mondiale, notamment les autorités et les opérateurs portuaires, les gouvernements nationaux, les organisations supranationales, les agences de développement, les divers intérêts maritimes et d'autres acteurs publics et privés du commerce, de la logistique et des services de la chaîne d'approvisionnement.
Le développement de l'ICPP repose sur le temps total passé par les porte-conteneurs dans les ports, de la manière expliquée dans les sections suivantes du rapport, et comme dans les itérations précédentes de l'ICPP. Cette quatrième itération utilise des données pour l'année civile complète 2023. Elle poursuit le changement introduit l'année dernière en n'incluant que les ports qui ont eu un minimum de 24 escales valides au cours de la période de 12 mois de l'étude. Le nombre de ports inclus dans l'ICPP 2023 est de 405.
Comme dans les éditions précédentes de l'ICPP, la production du classement fait appel à deux approches méthodologiques différentes : une approche administrative, ou technique, une méthodologie pragmatique reflétant les connaissances et le jugement des experts ; et une approche statistique, utilisant l'analyse factorielle (AF), ou plus précisément la factorisation matricielle. L'utilisation de ces deux approches vise à garantir que le classement des performances des ports à conteneurs reflète le plus fidèlement possible les performances réelles des ports, tout en étant statistiquement robuste.
Presentation by Herman Kienhuis (Curiosity VC) on Investing in AI for ABS Alu...Herman Kienhuis
Presentation by Herman Kienhuis (Curiosity VC) on developments in AI, the venture capital investment landscape and Curiosity VC's approach to investing, at the alumni event of Amsterdam Business School (University of Amsterdam) on June 13, 2024 in Amsterdam.
Prescriptive analytics BA4206 Anna University PPTFreelance
Business analysis - Prescriptive analytics Introduction to Prescriptive analytics
Prescriptive Modeling
Non Linear Optimization
Demonstrating Business Performance Improvement
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2. Climate Forecasting Unit
Fig. W1.1.1: Spring 10m wind resource (speed, m/s) availability from 1981-2011 (ERA-Interim)
m/s
Stage A: Wind Resource Assessment
Wind energy potential: Where is it the windiest?
Dark red regions of this map show where global 10m wind resource (speed, m/s) is highest in spring, and
lighter yellow regions where it is lowest.
N.b. This information is based on reanalysis* data (ERA-Interim) not direct observations.
* Reanalysis information comes from an objective combination of observations and numerical models that simulate one or more aspects of the Earth system, to
generate a synthesised estimate of the state of the climate system and how it changes over time.
SPRING Wind Forecasts
(March + April + May)
3. Climate Forecasting Unit
Fig. W1.1.2: Spring 10m wind resource inter-annual variability from 1981-2011 (ERA-Interim)
m/s
Stage A: Wind Resource Assessment
Wind energy volatility: Where does the wind vary the greatest?
Darker red regions of this map show where global 10m wind resource varies the most from one year to the
next in spring, and lighter yellow regions where it varies the least.
N.b. This information is based on reanalysis* data (ERA-Interim) not direct observations.
SPRING Wind Forecasts
(March + April + May)
4. Climate Forecasting Unit
Europe
Spring 10m wind resource availability Spring 10m wind resource inter-annual variabilitym/s
m/s
Areas of
interest: Patagonia/
E.Brasil
Central
Sahara/
Sahel
China/
Mongolia/
N. Russia
W.
Australia/
Tasmania
S.America Africa Asia Australia
N.Mexico/
N.Canada
N.America
UK/
Baltic Sea
Stage A: Wind Resource Assessment
Where is wind resource potential and variability (volatility) highest?
By comparing both the spring 10m global wind resource availability and inter-annual variability, it can be seen
that there are several key areas (listed above) where wind speed is both abundant and highly variable.
These regions are most vulnerable to wind resource variability over climate timescales, and are therefore of
greatest interest for seasonal forecasting in spring.
SPRING Wind Forecasts
(March + April + May)
5. Climate Forecasting Unit
Fig. W2.1.1: Spring 10m wind resource ensemble mean correlation
(ECMWF S4, 1 month forecast lead time, once a year from 1981-2010)
time
windspeed
forecast
+ 1.0
obs. forecast
- 1.0
forecast
example 1
forecast
- 1.0
example 2
example 3
SPRING Wind Forecasts
(March + April + May)
Stage B: Wind Forecast Skill Assessment
1St
validation of the climate forecast system:
The skill of a climate forecast system, to predict global wind speed variability in spring 1 month ahead, is
partially shown in this map. Skill is assessed by comparing the mean of a spring wind forecast, made every
year since 1981, to the reanalysis “observations” over the same period. If they follow the same variability over
time, the skill is positive. This is the case even if their magnitudes are different (see example 1 and 2).
Perfect
Forecast
Same as
Climatology
Worse
than
Clima-
tology
Can the wind forecast mean tell us about the
wind resource variability at a specific time?
6. Climate Forecasting Unit
Fig. W2.1.1: Spring 10m wind speed ensemble mean correlation
(ECMWF S4, 1 month forecast lead time, once a year from 1981-2010)
Stage B: Wind Forecast Skill Assessment
1St
validation of the climate forecast system:
Dark red regions of the map show where the climate forecast system demonstrates the highest skill in spring
seasonal forecasting, with a forecast issued 1 month in advance. White regions show where there is no
available forecast skill, and blue regions where the climate forecast system performs worse than a random
prediction. A skill of 1 corresponds to a climate forecast that can perfectly represent the past “observations”.
Perfect
Forecast
Same as
Climatology
Worse
than
Clima-
tology
SPRING Wind Forecasts
(March + April + May)
Can the wind forecast mean tell us about the
wind resource variability at a specific time?
7. Climate Forecasting Unit
Fig. W2.1.2: Spring 10m wind resource CR probability skill score
(ECMWF S4, 1 month forecast lead time, once a year from 1981-2010)
time
windspeed
forecast
+ 1.0
obs. forecast
- 1.0
forecast
example 1
forecast
- 1.0
example 2
example 3
Stage B: Wind Forecast Skill Assessment
2nd
validation of the climate forecast system:
The skill of a climate forecast system, to predict global wind resource variability in spring 1 month ahead, is
fully shown in this map. Here, skill is assessed by comparing the full distribution (not just the mean value as in
the previous map) of a spring wind forecast, made every year since 1981, to the “observations” over the same
period. If they follow the same magnitude of variability over time, the skill is positive (example 2).
SPRING Wind Forecasts
(March + April + May)
Perfect
Forecast
Same as
Climatology
Worse
than
Clima-
tology
Can the wind forecast distribution tell us about
the magnitude of the wind resource variability,
and its uncertainty at a specific time?
8. Climate Forecasting Unit
Fig. W2.1.2: Spring 10m wind resource CR probability skill score
(ECMWF S4, 1 month forecast lead time, once a year from 1981-2010)
Stage B: Wind Forecast Skill Assessment
2nd
validation of the climate forecast system:
Dark red regions of the map show where the climate forecast system demonstrates the highest skill in spring
seasonal forecasting, with a forecast issued 1 month in advance. White regions show where there is no
available forecast skill, and blue regions where the climate forecast system performs worse than a random
prediction. A skill of 1 corresponds to a climate forecast that can perfectly represent the past “observations”.
SPRING Wind Forecasts
(March + April + May)
Perfect
Forecast
Same as
Climatology
Worse
than
Clima-
tology
Can the wind forecast distribution tell us about
the magnitude of the wind resource variability,
and its uncertainty at a specific time?
9. Climate Forecasting Unit
Europe
Areas of
interest: E.Brasil/
N.Chile
Indonesia/
W.India
W.
Australia
S.America Africa Asia Australia
Mexico/
S.Canada
N.America
N.Spain/
S.E Europe
Spring 10m wind resource variability
magnitude, and its uncertainty forecast skill
Spring 10m wind resource variability
forecast skill
Wind resource variability
forecast skill only
Wind resource magnitude and its uncertainty forecast skill
Kenya/
Somalia
Stage B: Wind Forecast Skill Assessment Where is wind forecast skill highest?
By comparing both the spring 10m global wind resource forecast skill assessments, it can be seen that there
are several key areas (listed above) where wind resource forecasts are skilful in assessing its variability
magnitude and uncertainty. These regions show the greatest potential for the use of operational spring wind
forecasts, and are therefore of greatest interest to seasonal wind forecasting in spring.
SPRING Wind Forecasts
(March + April + May)
10. Climate Forecasting Unit
Stage B: Wind Forecast Skill Assessment
Magnitude and uncertainty forecast skillVariability forecast skill
m/sm/sm/s
SPRING Wind Forecasts
These four maps compare the seasonal spring 10m wind resource global forecast skill maps (bottom)
alongside the spring 10m global wind resource availability and inter-annual variability maps (top). It can be
seen that there are several key areas (highlighted above) where the forecast skill is high assessing its
variability, magnitude and uncertainty, and the wind resource is both abundant and highly variable. These
regions demonstrate where spring seasonal wind forecasts have the greatest potential for operational use.
EuropeAreas of
Interest:
(Forecast skill)
E.Brazil
N.Chile
Indonesia/
W.India
W.
S.America Africa Asia Australia
Mexico/
S.Canada
N.America
N.Spain/
S.E Europe
Kenya/
Somalia
Mexico E.Brasil/Mexico/ W.Australia
Europe S.America Africa Asia AustraliaN.America
Patagonia/
E.Brazil
C.Sahara/
Sahel
China/ Mongolia/
N.Russia
W.Australia/
Tasmania
N.Mexico/
N.Canada
UK/
Baltic Sea
Areas of
Interest:
(Resources)
N.Mexico/
E.Brasil
W.Australia
Where is wind resource potential and volatility highest?
Wind resource inter-annual variabilityWind resource availability
Stage A: Wind Resource Assessment
Variability forecast skill
Where is wind forecast skill highest?
SPRING Wind Forecasts
(March + April + May)
11. Climate Forecasting Unit
%
N.America
MexicoMexico
Areas of Interest Identified:
(Resources and Forecast Skill)
S.America
E.BrasilE.Brasil
W.
Australia
W.Australia
S.America
Fig. W3.1.1: Probabilistic forecast of (future) spring 2011,10m wind resource most likely tercile
(ECMWF S4, 1 month forecast lead time)
Stage C: Operational Wind Forecast
This operational wind forecast shows the probability of global 10m wind resource to be higher (red), lower
(blue) or normal (white) over the forthcoming spring season, compared to their mean value over the past 30
years. As the forecast season is spring 2011, this is an example of wind forecast information that could have
been available for use within a decision making process in February 2011.
SPRING Wind Forecasts
(March + April + May)
12. Climate Forecasting Unit
%
N.America
MexicoMexico
Areas of Interest Identified:
(Resources and Forecast Skill)
S.America
E.BrasilE.Brasil
W.
Australia
W.Australia
S.America
Stage C: Operational Wind Forecast
The key areas of highest interest are shown, identified in the stages A and B of the forecast methodology.
These regions demonstrate where spring seasonal 10m wind forecasts have the greatest value and potential
for operational use. The areas that are blanked out either have lower forecast skill in spring (Stage B) and/or
lower wind resource availability and inter-annual variability (Stage A).
SPRING Wind Forecasts
(March + April + May)
Fig. W3.1.1: Probabilistic forecast of (future) spring 2011,10m wind resource most likely tercile
(ECMWF S4, 1 month forecast lead time)
13. Climate Forecasting Unit
%
N.America
MexicoMexico
Areas of Interest Identified:
(Resources and Forecast Skill)
S.America
E.BrasilE.Brasil
W.
Australia
W.Australia
S.America
Stage C: Operational Wind Forecast
This does not mean that the blanked out areas are not useful, only that the operational wind forecast for these
regions should be used within a decision making process with due awareness to their corresponding
limitations. The primary limitations to a climate forecast are either the forecast skill and/or the low risk of
variability in the wind resource for a given region. See the “caveats” webpage for further limitations.
SPRING Wind Forecasts
(March + April + May)
Fig. W3.1.1: Probabilistic forecast of (future) spring 2011,10m wind resource most likely tercile
(ECMWF S4, 1 month forecast lead time)
14. Climate Forecasting Unit
The research leading to these results has received funding
from the European Union Seventh Framework Programme
(FP7/2007-2013) under the following projects:
CLIM-RUN, www.clim-run.eu (GA n° 265192)
EUPORIAS, www.euporias.eu (GA n° 308291)
SPECS, www.specs-fp7.eu (GA n° 308378)