Dear Mercatorians,
The effects of this 7th stage of the quarterly newsletter
will make themselves felt. It begins with a first category
article in which assimilation climbers will be able to
express their talents in fairly steep transitions. The quiz
which follows is a deceptive flat stretch which should
suit observation rollers. Participants will probably bunch
up again before the final sprint of this stage which is
almost entirely devoted to satellite alitmetry.
Gerard Llobet - Mesa redonda sobre Premio Nobel de Economía 2014, Jean TiroleFundación Ramón Areces
El 27 de octubre de 2014, la Fundación Ramón Areces organizó una Mesa Redonda sobre el Premio Nobel en Economía 2014, Jean Tirole. Con el título 'Análisis del poder del mercado y su regulación', intervinieron Luis Garicano, de la London School of Economics; Gerard Llobet, de CEMFI, y David Pérez Castrillo, de la Universidad Autónoma de Barcelona. Presentó el acto Samuel Bentolila, de CEMFI.
Gerard Llobet - Mesa redonda sobre Premio Nobel de Economía 2014, Jean TiroleFundación Ramón Areces
El 27 de octubre de 2014, la Fundación Ramón Areces organizó una Mesa Redonda sobre el Premio Nobel en Economía 2014, Jean Tirole. Con el título 'Análisis del poder del mercado y su regulación', intervinieron Luis Garicano, de la London School of Economics; Gerard Llobet, de CEMFI, y David Pérez Castrillo, de la Universidad Autónoma de Barcelona. Presentó el acto Samuel Bentolila, de CEMFI.
Dear Mercatorians,
Summer has come and its newsletter as well. This sixth
MERCATOR quarterly has a lot of different items on the
menu. The first article compares re-analysis data and
in-situ observations. Then come the 'fresh' products:
- MERCATOR forecasts as support for the recent
POSEIDON 284 campaign.
- Who, what, how... our quiz feature.
- MERCATOR forecasts and ocean yacht racing:
how useful are they?
So, adjust your deck chair and switch off your cell
phone. It's time for a good read.
Editorial – January 2012 – Various areas of benefit using the Mercator Ocean products
Greengs all,
Mercator Ocean runs operaonal services and provides experse to a large panel of users: sciensts, public authories, agencies and even the private
sector. This month’s newsle!er gives a focus on four areas of benefits. First arcle is dedicated to the contribuon of Météo-France and Mercator
Ocean to the research at sea of the wreckage from the Air France AF447 flight from Rio to Paris. Second arcle presents the contribuon of Mercator
Ocean and Laboratoire d’Aerologie in order to invesgate the dispersion in seawater of radionuclides a.er the castrophic event of the Fukushima
nuclear plant. Third arcle displays the work done at Mercator Ocean in order to assist Meteo France in predicng the fate of sea polluons
or dri.ing objects during disasters like oil spills for example. Last arcle is about the Mercator Ocean state of the art reanalysis product GLORYS2V1
which is of great interest for the climate community.
On the night of June 1st to June 2nd 2009 at 2h10 GMT, the Air France AF447 flight from Rio to Paris disappeared in a highly variable and poorly
observed part of the western tropical Atlanc Ocean. The two first phases of research at sea of the AF447 wreckage were both unsuccessful. The
“Bureau d’Enquêtes et d’Analyses pour la sécurité de l’aviaon civile” (BEA) (for the invesgaon of airplane accidents) decided in November 2009
to gather a group of ocean sciensts and mathemacians in order to prepare the third phase of research. The study performed by Mercator Océan
and Météo-France as part of this group is partly described here with a focus on the modelling part of the common contribuon of Météo-France and
Mercator Ocean as an a!empt to improve the currents and winds and consequently the dri. accuracy.
A.er the castrophic event of the Fukushima nuclear plant in March 11 2011, various simulaons using the 3D SIROCCO circulaon model were performed
in order to invesgate the dispersion in seawater of radionuclides emi!ed by the Fukushima nuclear plant. In this framework, Mercator
Ocean has provided the inial fields and the lateral open boundary condions from the global 1/12° system. Moreover, for the MyOcean component
of GMES, Mercator Ocean has also calculated the Lagrangian dri. of water parcles from the global 1/12° ocean system and has set up a weekly
web bullen of the situaon of currents published during one year from the date of the disaster.
Predicng the fate of sea polluons or dri.ing objects is a crucial need during disasters. In case of incident over the French marine territory, Météo
France has the responsibility to provide reliable ocean dri. forecasts for authories and decision makers using the oil spill model MOTHY which is
operated on duty 24/7/365. Since 2007, MOTHY is fed with currents forecasted by Mercator Ocean’s assimilated systems. Stephane Law Ch
Calculation of the Sun exposure for the synthesis of vitamin D in Urcuquí, Ec...Javier García Molleja
Authors: G.M. Salum, J. García Molleja, B.A. Regalado Díaz, L.A. Guerrero León, C. Berrezueta
Proceeding of abstracts of IWBBIO, ISBN: 978-84-16929-58-0 (2017) 27
Dear Mercatorians,
The time has come to talk about the second MERCATOR
prototype.
2002 marks a significant development with the
commissioning of the PAM model for operational
service. PAM (for Prototype Atlantique Méditerrannée)
offers a description of the North Atlantic and
Mediterranean regions with very high horizontal
resolution. This issue of the newsletter describes how it
is being implemented.
In addition, since the winter is now over, we’ll be
discussing winter convection with a comparison of
winter 2000/2001 and winter 20001/2002.
Naturally we have not forgotten our quiz, which has
been slipped in between the two articles.
Now that we’ve filled you in, sit back, put your feet up
and have a good read!
El equipo de expertos, entre los que se encuentra Francisco Espartero, ha descubierto una nueva lluvia de meteoros asociada al cometa 209P/LINEAR, que tendrá lugar todos los años durante el mes de mayo
Big data and remote sensing: A new software of ingestion IJECEIAES
Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.
First results from_the_hubble_opal_program_jupiter_in_2015Sérgio Sacani
Os cientistas usando o Telescópio Espacial Hubble da NASA/ESA produziram novos mapas de Júpiter, que mostram as contínuas mudanças que ocorrem com a famosa Grande Mancha Vermelha. As imagens também revelam uma rara estrutura em forma de onda na atmosfera do planeta que não tinha sido vista por décadas. A nova imagem é a primeira de uma série de retratos anuais dos planetas externos do Sistema Solar, que nos darão um novo olhar desses mundos remotos, e ajudarão os cientistas a estudarem como eles mudam com o passar do tempo.
Nessa nova imagem de Júpiter, uma grande quantidade de feições foi capturada incluindo ventos, nuvens e tempestades. Os cientistas por trás dessas novas imagens, as obtiveram usando a Wide Field Camera 3 do Hubble, num período de observação de mais de 10 horas e produziram assim dois mapas completos do planeta, a partir das suas observações. Esses mapas fizeram com que fosse possível determinar a velocidade dos ventos em Júpiter, com a finalidade de identificar diferentes fenômenos na sua atmosfera além de traquear as suas feições mais famosas.
As novas imagens confirmam que a grande tempestade que tem existido na superfície de nuvens de Júpiter por no mínimo 300 anos, continua a encolher, mas mesmo que desapareça, ela irá morrer lutando. A tempestade, conhecida como Grande Mancha Vermelha, é vista aqui fazendo seus movimentos em espiral no centro da imagem do planeta. Ela tem diminuído de tamanho de maneira muito rápida de ano em ano. Mas agora, a taxa de encolhimento parece ter reduzido novamente, mesmo apesar da mancha ser cerca de 240 quilômetros menor do que era em 2014.
This is the paper for our final project in our Numerical Weather Prediction class. For this project, we analyzed model output from a Nested Regional Climate Model (NRCM), which is an adaptation of the Advanced Research WRF (ARW). The model output variables analyzed were outgoing long wave radiation (OLR) and precipitation (convective plus non-convective). The goal of this research project was to determine why errors were occurring in the model, and what could be done to correct them. In this paper, we provide some insight into why these errors occurred, particularly errors within the model which equaled or surpassed the overall mean climate error.
PAPER: DETERMINATION OF THE EVAPOTRANSPIRATION OF THE HYDROGRAPHIC BASIN “HYD...Jaime Navía Téllez
PAPER;
DETERMINATION OF THE EVAPOTRANSPIRATION OF THE HYDROGRAPHIC BASIN “HYDROGRAPHIC UNIT 02229" THROUGH THE USE OF REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEMS;
Ingeniería Civil;
Civil engineering;
Ingeniería Ambiental;
Ambiental Engineering;
Ingeniería De Puentes;
Bridge Engineering;
S.I.G.;
Jaime Navía Téllez;
JNT;
J.N.T.;
ABSTRACT
Evapotranspiration is defined as the loss of moisture from a surface by direct evaporation together with the loss of water by transpiration from vegetation. It is expressed in millimeters per unit of time.
Evapotranspiration monitoring has important implications for global and regional climate and hydrological cycle modelling, as well as for advising on environmental stress affecting agricultural and forest ecosystems. Remote sensing and GIS are currently the only technologies capable of providing the necessary measurements for the global and economically feasible calculation of evapotranspiration.
The information of energy or radiance emitted and reflected by the earth's surface provided by satellites such as Landsat, with a pixel of 30 meters of spatial resolution, has been one of the most used (Chuvieco 2002). The Landsat TM (Thematic Mapper) 5 and Landsat 7 ETM + satellites have images that cover all the regions in different seasons of the year, with a frequency or temporal resolution of 16 days.
This paper presents a methodology based on the method proposed by Seguin and Itier (1989) and Vidal and Perrier (1992) for the determination of real evapotranspiration (ETR) at a regional scale, of the basin "hydrographic unit 02229" located in the city of Oruro, through the use of a time series of four images from the Landsat-8 ETM LC08_L1TP_RT satellite and an ALOS PALSAR image and the Geographic Information Systems. The result of this analysis consists of a set of ETd GIS layers that have 30 meters of spatial resolution (total area of 1788 km2) with almost monthly temporal resolution. The methodology proposed by Seguin and Itier (1989) and Vidal and Perrier (1992) has been used, which requires three main variables to calculate the ETd: the temperature of the earth's surface, the air temperature and the net radiation. The temperature of the earth's surface has been obtained by correcting the emissivity of the Landsat-8 ETM thermal band. Air temperature has been calculated by multiple regression analysis and spatial interpolation of meteorological ground stations in the satellite path (Ninyerola et al., 2000). The net radiation has been calculated by means of the radius balance. These preliminary results are very interesting due to the difficulty in obtaining ETd data from forests and crops and the high spatial and temporal resolution used.
Keywords: Evapotranspiration, Net Radiation, Remote Sensing, Landsat.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Dear Mercatorians,
Summer has come and its newsletter as well. This sixth
MERCATOR quarterly has a lot of different items on the
menu. The first article compares re-analysis data and
in-situ observations. Then come the 'fresh' products:
- MERCATOR forecasts as support for the recent
POSEIDON 284 campaign.
- Who, what, how... our quiz feature.
- MERCATOR forecasts and ocean yacht racing:
how useful are they?
So, adjust your deck chair and switch off your cell
phone. It's time for a good read.
Editorial – January 2012 – Various areas of benefit using the Mercator Ocean products
Greengs all,
Mercator Ocean runs operaonal services and provides experse to a large panel of users: sciensts, public authories, agencies and even the private
sector. This month’s newsle!er gives a focus on four areas of benefits. First arcle is dedicated to the contribuon of Météo-France and Mercator
Ocean to the research at sea of the wreckage from the Air France AF447 flight from Rio to Paris. Second arcle presents the contribuon of Mercator
Ocean and Laboratoire d’Aerologie in order to invesgate the dispersion in seawater of radionuclides a.er the castrophic event of the Fukushima
nuclear plant. Third arcle displays the work done at Mercator Ocean in order to assist Meteo France in predicng the fate of sea polluons
or dri.ing objects during disasters like oil spills for example. Last arcle is about the Mercator Ocean state of the art reanalysis product GLORYS2V1
which is of great interest for the climate community.
On the night of June 1st to June 2nd 2009 at 2h10 GMT, the Air France AF447 flight from Rio to Paris disappeared in a highly variable and poorly
observed part of the western tropical Atlanc Ocean. The two first phases of research at sea of the AF447 wreckage were both unsuccessful. The
“Bureau d’Enquêtes et d’Analyses pour la sécurité de l’aviaon civile” (BEA) (for the invesgaon of airplane accidents) decided in November 2009
to gather a group of ocean sciensts and mathemacians in order to prepare the third phase of research. The study performed by Mercator Océan
and Météo-France as part of this group is partly described here with a focus on the modelling part of the common contribuon of Météo-France and
Mercator Ocean as an a!empt to improve the currents and winds and consequently the dri. accuracy.
A.er the castrophic event of the Fukushima nuclear plant in March 11 2011, various simulaons using the 3D SIROCCO circulaon model were performed
in order to invesgate the dispersion in seawater of radionuclides emi!ed by the Fukushima nuclear plant. In this framework, Mercator
Ocean has provided the inial fields and the lateral open boundary condions from the global 1/12° system. Moreover, for the MyOcean component
of GMES, Mercator Ocean has also calculated the Lagrangian dri. of water parcles from the global 1/12° ocean system and has set up a weekly
web bullen of the situaon of currents published during one year from the date of the disaster.
Predicng the fate of sea polluons or dri.ing objects is a crucial need during disasters. In case of incident over the French marine territory, Météo
France has the responsibility to provide reliable ocean dri. forecasts for authories and decision makers using the oil spill model MOTHY which is
operated on duty 24/7/365. Since 2007, MOTHY is fed with currents forecasted by Mercator Ocean’s assimilated systems. Stephane Law Ch
Calculation of the Sun exposure for the synthesis of vitamin D in Urcuquí, Ec...Javier García Molleja
Authors: G.M. Salum, J. García Molleja, B.A. Regalado Díaz, L.A. Guerrero León, C. Berrezueta
Proceeding of abstracts of IWBBIO, ISBN: 978-84-16929-58-0 (2017) 27
Dear Mercatorians,
The time has come to talk about the second MERCATOR
prototype.
2002 marks a significant development with the
commissioning of the PAM model for operational
service. PAM (for Prototype Atlantique Méditerrannée)
offers a description of the North Atlantic and
Mediterranean regions with very high horizontal
resolution. This issue of the newsletter describes how it
is being implemented.
In addition, since the winter is now over, we’ll be
discussing winter convection with a comparison of
winter 2000/2001 and winter 20001/2002.
Naturally we have not forgotten our quiz, which has
been slipped in between the two articles.
Now that we’ve filled you in, sit back, put your feet up
and have a good read!
El equipo de expertos, entre los que se encuentra Francisco Espartero, ha descubierto una nueva lluvia de meteoros asociada al cometa 209P/LINEAR, que tendrá lugar todos los años durante el mes de mayo
Big data and remote sensing: A new software of ingestion IJECEIAES
Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.
First results from_the_hubble_opal_program_jupiter_in_2015Sérgio Sacani
Os cientistas usando o Telescópio Espacial Hubble da NASA/ESA produziram novos mapas de Júpiter, que mostram as contínuas mudanças que ocorrem com a famosa Grande Mancha Vermelha. As imagens também revelam uma rara estrutura em forma de onda na atmosfera do planeta que não tinha sido vista por décadas. A nova imagem é a primeira de uma série de retratos anuais dos planetas externos do Sistema Solar, que nos darão um novo olhar desses mundos remotos, e ajudarão os cientistas a estudarem como eles mudam com o passar do tempo.
Nessa nova imagem de Júpiter, uma grande quantidade de feições foi capturada incluindo ventos, nuvens e tempestades. Os cientistas por trás dessas novas imagens, as obtiveram usando a Wide Field Camera 3 do Hubble, num período de observação de mais de 10 horas e produziram assim dois mapas completos do planeta, a partir das suas observações. Esses mapas fizeram com que fosse possível determinar a velocidade dos ventos em Júpiter, com a finalidade de identificar diferentes fenômenos na sua atmosfera além de traquear as suas feições mais famosas.
As novas imagens confirmam que a grande tempestade que tem existido na superfície de nuvens de Júpiter por no mínimo 300 anos, continua a encolher, mas mesmo que desapareça, ela irá morrer lutando. A tempestade, conhecida como Grande Mancha Vermelha, é vista aqui fazendo seus movimentos em espiral no centro da imagem do planeta. Ela tem diminuído de tamanho de maneira muito rápida de ano em ano. Mas agora, a taxa de encolhimento parece ter reduzido novamente, mesmo apesar da mancha ser cerca de 240 quilômetros menor do que era em 2014.
This is the paper for our final project in our Numerical Weather Prediction class. For this project, we analyzed model output from a Nested Regional Climate Model (NRCM), which is an adaptation of the Advanced Research WRF (ARW). The model output variables analyzed were outgoing long wave radiation (OLR) and precipitation (convective plus non-convective). The goal of this research project was to determine why errors were occurring in the model, and what could be done to correct them. In this paper, we provide some insight into why these errors occurred, particularly errors within the model which equaled or surpassed the overall mean climate error.
PAPER: DETERMINATION OF THE EVAPOTRANSPIRATION OF THE HYDROGRAPHIC BASIN “HYD...Jaime Navía Téllez
PAPER;
DETERMINATION OF THE EVAPOTRANSPIRATION OF THE HYDROGRAPHIC BASIN “HYDROGRAPHIC UNIT 02229" THROUGH THE USE OF REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEMS;
Ingeniería Civil;
Civil engineering;
Ingeniería Ambiental;
Ambiental Engineering;
Ingeniería De Puentes;
Bridge Engineering;
S.I.G.;
Jaime Navía Téllez;
JNT;
J.N.T.;
ABSTRACT
Evapotranspiration is defined as the loss of moisture from a surface by direct evaporation together with the loss of water by transpiration from vegetation. It is expressed in millimeters per unit of time.
Evapotranspiration monitoring has important implications for global and regional climate and hydrological cycle modelling, as well as for advising on environmental stress affecting agricultural and forest ecosystems. Remote sensing and GIS are currently the only technologies capable of providing the necessary measurements for the global and economically feasible calculation of evapotranspiration.
The information of energy or radiance emitted and reflected by the earth's surface provided by satellites such as Landsat, with a pixel of 30 meters of spatial resolution, has been one of the most used (Chuvieco 2002). The Landsat TM (Thematic Mapper) 5 and Landsat 7 ETM + satellites have images that cover all the regions in different seasons of the year, with a frequency or temporal resolution of 16 days.
This paper presents a methodology based on the method proposed by Seguin and Itier (1989) and Vidal and Perrier (1992) for the determination of real evapotranspiration (ETR) at a regional scale, of the basin "hydrographic unit 02229" located in the city of Oruro, through the use of a time series of four images from the Landsat-8 ETM LC08_L1TP_RT satellite and an ALOS PALSAR image and the Geographic Information Systems. The result of this analysis consists of a set of ETd GIS layers that have 30 meters of spatial resolution (total area of 1788 km2) with almost monthly temporal resolution. The methodology proposed by Seguin and Itier (1989) and Vidal and Perrier (1992) has been used, which requires three main variables to calculate the ETd: the temperature of the earth's surface, the air temperature and the net radiation. The temperature of the earth's surface has been obtained by correcting the emissivity of the Landsat-8 ETM thermal band. Air temperature has been calculated by multiple regression analysis and spatial interpolation of meteorological ground stations in the satellite path (Ninyerola et al., 2000). The net radiation has been calculated by means of the radius balance. These preliminary results are very interesting due to the difficulty in obtaining ETd data from forests and crops and the high spatial and temporal resolution used.
Keywords: Evapotranspiration, Net Radiation, Remote Sensing, Landsat.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
Follow us on: Pinterest
Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
Natural farming @ Dr. Siddhartha S. Jena.pptxsidjena70
A brief about organic farming/ Natural farming/ Zero budget natural farming/ Subash Palekar Natural farming which keeps us and environment safe and healthy. Next gen Agricultural practices of chemical free farming.
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
1. The MERCATOR
Quarterly newsletter
#7- October 2002 - page 1
CNES
CNRS/INSU
IFREMER
IRD
METEO-FRANCE
SHOM
The riddle
By Eric Greiner (5-7-2002)
Introduction
In 2001 (30 May and 6 June) the operational PSY1-v1 system team detected an anomaly in the level of the sea
analysed to the south of the Gulf of Guinea, near Ascension island. It appeared to be an isolated case to the
extent that the 1993-99 reanalysis did not reveal any such malfunction. However, in 2002 the phenomenon
occurred again in a spectacular way successively on the 1, 8, 15 and 22 May. Fishermen using MERCATOR
products and CATSAT products (maps showing sea level, surface temperature and chlorophyl content) also
observed the anomaly: in May 2002, the sea level analysed on day D and forecast for D+7 and D+14 was
systematically too high in the Gulf of Guinea when compared to satellite altimetery observations (Topex-Poseidon
and ERS-2). An explanation is therefore called for. Is there a problem with the projected atmospheric forcing? If
this were the case, how would this affect the analysis on day D? And would it not also affect the later analyses
(D+7 or D+14)? This is the riddle we shall now attempt to solve.
Initial observations
The rest of the discussion will deal with the analysis date of 8 May 2002, on which the most spectacular anomaly
was detected. It may be seen that the anomalies on the 1, 15 and 22 May are similar to those of 8 May, with a
few slight differences.
Editorial Contents
Dear Mercatorians,
The effects of this 7th stage of the quarterly newsletter
will make themselves felt. It begins with a first category
article in which assimilation climbers will be able to
express their talents in fairly steep transitions. The quiz
which follows is a deceptive flat stretch which should
suit observation rollers. Participants will probably bunch
up again before the final sprint of this stage which is
almost entirely devoted to satellite alitmetry.
The riddle
Introduction
Initial observations
A clue
Following the tracks
The gigantic stroboscope
What about the model's instability?
How did the anomaly affect the forecast?
What should be done?
A few lessons to be learnt
Quizz
A posteriori validation of hypotheses: correlation
radii
Notebook
2. The MERCATOR
Quarterly newsletter
#7- October 2002 - page 2
CNES
CNRS/INSU
IFREMER
IRD
METEO-FRANCE
SHOM
The riddle (continued)
Our investigation begins with the abnormally high sea level in the Gulf of Guinea (figure 1). The figures are taken
from the 'assimilation technical report' which was published in real time with the ocean bulletin on 8 May.
We can see that the sea level analysed for day D is particularly high for the whole of the Gulf of Guinea and even
further to the south and west.
The predicted sea level at D+7 is lower, but is still abnormally high when compared to satellite observations.
The predicted sea level at D+7 is once again similar to observations in the Gulf of Guinea.
The anomaly sequence may thus be summed up as a rising of the Gulf of Guinea during the day D analysis
followed by a gradual return to normal at D+7 and D+14. This adjustment to the shock caused by the
analysis on 8 May 2002 was done at the expense of various ocean processes such as the coastal Kelvin wave
from the Ivory Coast to Morocco (shown in red in figure 1).
Figure 1: analysed sea level (on left), predicted at D+7 (middle) and predicted at D+14 (right)
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The riddle (continued)
A clue
We will attempt to clarify what was happening during the 8 May analysis by looking closer at the sea level time
series at different places in the Gulf of Guinea (moorings 18, 19 and 22 of PSY1-v1). For the mooring at 8°W we
can see that the guess marked 'E' (forecast for day D or 'guess') has a value of about +5 cm. The analysis gives a
level of +15cm, which means an analysis jump of +10cm. The following forecast, as we have seen on the maps,
shows a drop in level up to 22 May. It may be observed as well that the forecast is consistently higher (we could
say 'warmer' to give a more vivid picture) than the hindcast from 1 May to 29 May. In short, the forecast is too
warm in May 2002. In 2001, this phenomenon only lasted 2 weeks.
When compared with the maps, it is clearer that the
forecast does not exactly 'match' the hindcast. In other
words, the analysis jump on day D not only introduced
a bias (shift) in the forecast but also affected the
dynamics. More precisely, the drops in level are less
significant in the forecast than in the hindcast,
which is true for all 3 moorings. There is thus a ripple
effect of the analysis on the forecast. This will be
discussed later.
What we especially have to note for the moment is the
difference between the analysis on day D and that,
deferred for day D+7. Thus, while the analysis jump on
day D for 8 May was +10cm, the hindcast jump (done
on D+7) for 8 May was nil. In other words, the real-
time analysis 'jumps' and the hindcast does not.
In fact the hindcast is continuous, as if it were perfect
or knew nothing of the observations. The problem
therefore is to find out what difference there is
between the real-time and the hindcasts.
Figure 2:
time series of sea level at 3 mooring points: 8°W-0°N
(18, top), 6.2°E-0.2°N (19, Sao Tomé, middle) and
14.3°W-7.6°S (22, Ascension island, bottom). The
unbroken curve shows the period preceding the
analysis (deferred time or 'hindcast' analysis) and the
dotted curve the forecast. The vertical lines show the
analysis dates. The colours correspond to the different
dates (dark blue for 17-4, blue-green (cyan) for 24-4,
aquamarine for 1-5).
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The riddle (continued)
Following the tracks
The difference is in the amount of observations. For the week preceeding dat D (figure 3, on left), we only have
observations up to D-3 (which is what we call the 'cutoff' date): the data acquisition has to be cut off to start the
analysis). Analysis one week later is called a hindcast, and all satellite altimetry observations are fed into the
acquisition system. In passing we can see why analysis for day D is trickier than for D+7 as there are many more
gaps between the tracks. But how do these gaps throw the system off?
Figure 3: sea level: observations from D-7 to day D (8 May 2002)
in real time (on left) and hindcast (on right)
The gigantic stroboscope
The trick in fact is that the problem is not caused by spatial gaps, but by the time interval between tracks. To
understand this point, you have to look at the difference between the observations and the model (innovation or
misfit) in figure 4. In real time, from D-7 to D-3 (i.e. from 1 May to 5 May), the observations are almost all higher
than the model (shown in red, figure 4 on left). There is not much inconsistent information such as the two
neighbouring tracks around 15°W. In deferred time, from D-7 to D (from 1 May to 8 May), there are now as
many lower observations as for the model. In other words, the information for the periods [D-7 , D-3] and [D-3,
D] is the opposite. We may note in passing that the same phenomenon occurs around 40°W-15°N.
Figure 4: sea level: difference between observations minus model from D-7 to day D (8 May 2002)
in real time (on left) and hindcasting (on right)
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The riddle (continued)
A synthesis of the anomaly is shown in figure 5. With a 7 day cycle and a cutoff of 3, with an oscillatory
phenomenon with a 7 day frequence, only half of the phenomenon can be observed in real time. For the analysis
tool , the model gives readings which are obviously much lower than the observations and it thus has to be 'lifted'
during analysis. The analysis jump is in itself a big one. It is quite natural even and is only wrong to the extent
that the phenomenon's frequency is slightly higher than the cutoff. During hindcasting, we can observe the low
and the high phases of the phenomenon at the same time, and the analysis tool does not, therefore, lift the
model, since, on average, the model's pseudo observations fall in the middle of the actual observations. There is
no analysis jump.
Figure 5: difference in observations between real time model (on left) and hindcast model (on right):
the 3 day cutoff creates aliasing for a phenomenon with a 7 day frequency.
We can finish the analysis of this anomaly by noting
that this type of malfunction can only occur in real time
and only for a narrow range of frequencies. Hence
figure 6 shows that the 3 day cutoff only creates
significant aliasing for frequencies from between 5 and
12 days.
Figure 6:
difference in observations - real time model: impact of
3 day cutoff on phenomenon with different frequencies.
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The riddle (continued)
What about the model's instability?
LThe winds from the European Centre for medium range weather forecasting (ECMWF), which were used to force
the ocean model, revealed a clear change between the end of April and the beginning of May. Around 20 April,
the tradewinds were strong in the north (around 40°-50°W and 4°-14°N) and weak in the south, with a calm
zone on the equator and in the Gulf of Guinea.
Figure 7: Zonal wind stress from the European Centre
In less than two weeks, we witnessed a shift in strength of the tradewinds from north to south. During this shift,
a small scale wave can be seen on the equator (figure 7) to the west around 23 April and then the east around 26
April. By the beginning of May the shift had been completed, with strong tradewinds in the south.
Figure 8: Zonal wind stress from the European Centre at 1°30'N from west to east on 23 April and 5 May
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The riddle (continued)
If we look at the section of zonal wind stress at 1°30'N (figure 8) we can see quite clearly that there are
structures in the atmospheric forcing which are capable of generating equatorial waves (Kelvin) in the ocean
model. Nevertheless the model does not give a clear response at the equator.
On the other hand, around 12°S, we can see on the wind maps (figure 7) that the strengthening of the southerly
tradewinds is not homogeneous in longitude but occurs through (among others) a west to east rising propagation.
In this case the model's response is quite clear (figure 9). The sudden (7 days) drop in sea level in the Gulf of
Guinea (and beyond) is the basin response to the wind. We then check that the correlation between sea level and
zonal wind stress is satisfactory for the whole period (towards Ascension island).
Figure 9: sea level on 30 April and 4 May
Figure 11: change in sea level between 30 April and 5 May barocline height and barotrope height
The model response is thus mechanical and local.
Unfortunately we cannot compare the wind stresses
from the European centre with the PIRATE moorings
since the anemometers located at 10°W-6°S, 10°W-0°
N, 0°W-0°N were not in operation at the beginning of
2002 (which also means they were not available from
the European Centre). It is nevertheless possible to
confirm that there is no clear ocean response to this
atmospheric forcing (figure 10).
It is possible to confirm that the temperature time
series from the hindcast shows no significant
fluctuations and that is is comparable to the PIRATA
values. So where does the model's sea level response
come from? Figure 11 shows the change in sea level
between 30 April and 5 May in analytical terms. The
barocline height response (or dynamic height, deduced
from temperature and salinity) has been separated
from the barotrope height (deduced from the barotrope
current). It may be seen that the barocline response is
located near the equator It is a fairly moderate
response. On the other hand, the barotrope response is
very intense around 15°S, in other words, very near to
Ascension island and just above the mid-Atlantic
dorsal.
Figure 10: temperature time-series at the PIRATA
mooring at 10°W-6°S
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The riddle (continued)
How did the anomaly affect the forecast?
We noticed at the beginning that the analysis did not only introduce a warm bias. We can also see that the
consecutive drops in level are less significant for the forecast than for the hindcast . This is very clear at
Ascension island and to a lesser extent at 8°W-0°N. We shall now attempt to determine which processes are
involved in this reaction.
To do this, we'll look at the temperature and meridianal speed time series at Ascension island (figure 12).
Figure 12: time series at Ascension island:
temperature (top) and meridianal speed (bottom), for hindcast (on left) and reanalysis (on right)
The first thing we notice is that there are no major differences between the hindcasst and that of the reanalysis.
There is no 7 day signal which could result in variations in sea level of about 25 cm. The two clear effects
revealed by an analysis of the forecast are on the one hand the lack of surface cooling (already noted) and on the
other an end to cooling at a depth of about 1000 m.
The meridianal speed of the hindcast has a very clear 7 day barotropic signal (from 1 to 15 May in particular).
This same signal is found in the barotropic height and in the sea level. In passing we can see the surface
advection is weak but oriented to the north (cold waters) in the reanalysis just after 8 May.
It may be seen that the 8 May analysis 'interrupts' the 7 day signal almost entirely. The forecast reveals a less
barotropic meridianal speed , whose signature in sea height is more neutral thus marking the end of oscillations
in this region.
Finally the surface advection is strong and oriented to the south (warm waters) in the forecast. Hence the surface
advection is the cause of heating in this region.
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The riddle (continued)
At the equator we can confirm that at 8°W the 7 day signal comes from fluctuations in the position of the
Equatorial Under Current (EUC) The latter "surfaces" in the hindcast, bringing its cold waters to the surface.
We know that the real-time analysis introduces a warm bias in the model. In practice this results in a lowering of
the thermohaline structure in the Gulf of Guinea at the equator. But this means a decrease in the zonal density
gradient. As this gradient drives the EUC at the centre of the basin, the EUC is interrupted by the analysis. This
then enables the southern equatorial current (SEC) to renew its intensity at the surface. This current oriented
towards the west advects the warm waters of the Gulf of Guinea. Hence the surface advection is also behind the
heating in this region.
What should be done?
The anomaly which has just been described can only occur for a very specific case of the analysis/forecasting
system. We can diminish the probability of this combination by improving each weak point. The first step is to try
and make the model more stable for this region for May.
In the number 3 Quarterly letter we already saw that the tropical instability waves (TIW) are too great as shown
by the summer surface temperatures in this configuration of the model. This is partly explained by an EUC which
is too fast by approximately 20 cm/s.
Some improvements have already been made to the next multi-variate PSY2-V1 operational system: increased
vertical diffusion at the bottom, shorter spin-up, new altimetry reference level. Via either the model or the
assimilation, this should help to limit shearing between SEC, EUC and NECC and hence reduce instability in this
region. Other possibilities are being considered (aerodynamic formulae for the ocean/atmosphere flux, increased
vertical turbulence during heating periods, types of water, etc.).
Another improvement is the cut-off which can be reduced. The orbit determination however requires a minimum
of time and so it is not easy to solve the cut-off problem. On the other hand, it is possible to lessen its impact by
using an extra satellite (Geosat Follow On for instance).
It is in fact possible that with only two satellites, the last track for any given region would be for J-4 or J-5.
Adding a satellite means we don't have to artificially increase the cut-off. The use of in-situ observations affected
by a 24h cut-off would also help matters.
A few lessons to be learnt
The model is too unstable in May, with a resonance peak at 7 days.
For May 2002, the real-time analysis degrades the projected fields by introducing a warm bias. The
hindcast does not correct the model's resonance at 7 days.
The cut-off is too far removed (J-3) from the analysis date. It would be a good idea to reduce it to
improve real-time analysis for frequencies of approximately 7 days. The use of a third satellite such as
Geosat Follow ON (GFO) would enable more observations just before cutoff.
There may be an alias for the observation/analysis system for phenomena whose period is between 5 and
12 days (which gives a poor sampling due to the cut-off during an analysis cycle).
It would be better to use hindcasts for applications which are not constrained to real-time.
The PSY1-v1 operational system with hindcasting (J-21) rather than just using the last analysis (J-7)
guarantees the system's stability.
This study was triggered by precise feedback from users. This shows the importance of feedback for
measuring and improving system performance.
10. The MERCATOR
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Quizz
More than 30 years after humans first walked on the moon, they've rolled on MERCATOR. This can be seen from
the 'tyre tracks' found at 40°W on the analysis increment for 11-9-2002:
sea level: analysis increment (correction)
on 11-9-2002 for 11-9-2002
Who dared to do such a thing?
You'll find the answer on the next page
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Quizz: Answer
Luckily, it was ERS-2 which rolled on MERCATOR. What happened was that an error crept in during processing of
altimetry observations from JASON, which has replaced Topex/Poseidon for the analysis since July. Delivery of
these observations to MERCATOR was delayed beyond the cut-off and the observations did not therefore arrive in
time to be taken into account by the analysis/forecasting system:
sea level: the week's observations for 11-9-2002
This was the first time this had occurred since the commissioning of PSY1-v1 in January 2001. It allowed us to
measure the impact of the operational system on a reduced altimetry coverage. We can clearly see how the
analysis was limited to the neighbourhood of the satellite tracks as the increment was nil in the 'gaps'. Where
there was too much space between the tracks in relation to the correlation radii, the altimetry plots can be seen
in the analysis increment. We should however note that the amplitude of the increment is fairly moderate and
that the tyre tracks are less visible in the absolute height field.
The following week, all of the week's observations on 11-9-2002 were entered into the system to produce a much
higher quality analysis increment in hindcasting.
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A posteriori validation of hypotheses:
correlation radii
By Eric Greiner
When defining a configuration for a numerical ocean model some algorithms have to be defined, for instance for
diffusion formulae, atmospheric forcing, physical parameter settings, etc. The relevance of these algorithms are
then assessed by validating the results of the numerical simulation, possibly by adjusting the algorithms. This
same validation may be done for the analysis part of the forecasting analysis system. This is because certain
choices also have to be made when defining the analysis tool, in particular for statistical hypotheses.
In an analysis system based on optimal interpolation (OI), the most important hypotheses are those for
observation and forecasting error covariance (guess). To reduce the numerical cost of OI, the covariances are
separated into variances and correlations. Each of the hypotheses for observation and guess variances and
correlation can and must be validated a posteriori. We shall now look at the case of correlation radii for the guess
error.
The PSY1-v1 radii were calculated a priori from sea level anomalies (SLA) observed by Topex/Poseidon. There are
several ways of calculating the correlation scale at a given location.
This scale is defined here as the furthest point at which the temporal correlation with the origin falls within the
interval [0.4-0.6]. Below this scale, the correlation is significant, beyond it we find decorrelation (in the form of a
bell).
The radius thus determines a circle within which the signal is significantly correlated, while signals outside the
circle are decorrelated.
The scales were calculated for each of 5 Topex/Poseidon years from 1993 to 1997, including the seasonal cycle,
then averaged over the 5 years and finally filtered to eliminate the small scales (Figure 2a).
The longitude correlation radii of the SLA observed (Figure 2a) can be seen in the SLA analysed for 2001 (Figure
2b).
The correlation radii deduced from the 3 months of summer in 2001 (Figure 2c) reveal a few significant
differences from the imposed radii. The same is true for the three months of winter in 2001.
The correlation radii deduced from the SLA analysis increments in 2001 (Figure 2d) are clearly different from
those imposed.
In PSY1-v1, the correlation structure for the guess has
the form of a spatio-temporal bell which varies in
shape according to the geographical position. Thus the
correlation is approximately 0.2 between two points
separated by a distance 'dx' which would be twice the
correlation radius (Figure 1). This correlation function
does not have a negative lobe as is often the case in
meteorology. This means that the analysis is less
'refined' (smoother). The advantage is that it limits
errors due to extrapolation between satellite tracks.
In PSY1-v1, the correlation radius of the guess
determines the sphere of influence of an individual
observation, since the observations are assumed to not
be correlated among themselves (observation error
correlation in the form of a dirac distribution).
Hence, in the riddle, the width of the 'tyre tracks' is
determined by the correlation radius of the guess. Figure 1: function of the correlation of PSY1-v1
as a function of 'dx', the distance normalized by the
zonal correlation radius.
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A posteriori validation of hypotheses: correlation radii (continued)
The same may be said for the meridianal correlation radii observed (Figure 3a) which can be seen in the SLA
analysed for 2001 (Figure 3b).
Although there appears to be a meridianal correlation maximum off the coasts of Portugal and Africa, which looks
like an ERS satellite track, this is in fact not the case. This strong consistency of the correlation is related to the
coherent structure of the meridian current between 1000 m and 2000 m, a current which is strongly constrained
by the bathymetrics on the edge of the continental shelf.
The correlation radii deduced from the analysis increments in 2001 (Figure 3c) are clearly different from those
imposed.
Figure 2: Zonal correlation radii deduced from Topex/Poseidon (a, top left),
calculated a posteriori from the hindcast for 2001 (b, top right),
calculated a posteriori for July to September 2001 (c, bottom left),
calculated a posterior from the analysis increments for 2001 (d, bottom right).
Figure 3: Zonal correlation radii deduced from Topex/Poseidon (a, left),
calculated a posteriori from the hindcast for 2001 (b, right),
calculated a posterior from the analysis increments for 2001 (c, right).
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A posteriori validation of hypotheses: correlation radii (continued)
Figure 4: zonal correlation radius (on left) and meriain correlation radius (on right)
calculated a posteriori on hindcast increments for 2002 and averaged in longitude.
The correlation radii for the guess are an important
part of this system. However, most of the analysis
tools similar to the OI tool have to specify, in one way
or another, statistics which describe the error
covariances for the guess . The important thing is to
choose a 'good' set of system states, in other words
one which has realistic statistics. The first solution
consists in using a constant correlation radius
(~200km). This value is often deduced from a set of
empirical experiments.
A second solution involves deducing correlation radii
directly from observations: which is what was done for
PSY1-v1.
Figures 2a and 2 b, or 31 and 3b show that this choice
is compatible with the analysis tool and the ocean
model. The correlation radii for the observed SLA and
the analysed SLA are similar, even though the
observed variance remains much higher than the
analysed variance. Regions for which there are
differences are Ascension island, the north of the West
Indies Arc and also the continental shelves of Iceland,
Greenland and Labrador. This model configuration
(bathymetric trapping , sea ice) only gives a poor
reproduction of all these regions.
It may be noted that the radii deduced a posteriori
from the summer months (Figure 2c) are different to
those deduced for a whole year. They are generally
smaller. This is due to the fact that the steric signal
(dilatation due to summer warming) is a large scale
signal which accounts for a lot of the annual signal.
By choosing a set from a single season we are able to
filter out a goodly proportion of the steric signal for the
statistics to be deduced from this set. Likewise, it may
be noted that the correlation radius maximum around
40°W-4°N, deduced for 2001 (figure 2b) disappears
from the radius deduced from the summer months
(figure 2c). This is normal since the signal comes from
the meridianal migration of structures in this region,
which is very clearly visible between October and
January.
A third solution consists in using seasonal sets or in
filtering a seasonal signal or even a frequency band we
might not want to correct from a multi-annual set.
A fourth solution consists in using a set of analysis
increments. The assumption then is that the analysis is
sufficiently realistic for the increments to be
representative of the forecasting error. We can also
use a seasonal set of increments. The correlation radii
deduced from SLA increments (figures 2d, 2c) are
often much smaller than in the analysed SLA (figures
2b, 3b). For the zonal average of increment correlation
radii (figure 4) we can see that the increment
correlation structures are almost isotropic except for
the 5°N-10°S equatorial strip.
We then check a posteriori to ensure that 220-250km
is in fact a good mean value for a correlation radius.
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A posteriori validation of hypotheses: correlation radii (continued)
It may be noted that while the increment correlation radii are generally smaller than those for the analysed SLA
(for instance around 25°W-39°N) the opposite may also hold (for instance around 25°W-0°N). We can explain
these differences in system behaviour by means of SLA time series and SLA increments near to the two locations
(figure 5).
Figure 5: SLA time series (on left) and SLA increments (on right)
around 25°W-0°N (top) and around 25°W-39°N (bottom) for the hindcast for 2001.
Around 25°W-0°N there is a slight bi-annual signal. The increments have the same amplitude as the signal which
means that there is a significant relative error. Paradoxically the increments are better correlated than the SLA.
Due to local effects (meridianal migration) increment correlation for September, October and November is
lessened and modulated at a smaller scale.
Around 25°W-39°N there is a clear annual cycle and approximately a one month delay between 25°W and 20°W.
The increment has a lower amplitude than the signal and the relative error is thus small. In spite of specifying
fairly large radii, the resulting increments are on a smaller scale than the SLA.
This is due to the high density of satellite observations in this region (Topex-Poseidon and ERS-2).
The analysis tool thus does not really have to extrapolate between the tracks.
The correlation structure is thus less significant since the observations are used locally.
Conclusion
The compared observed SLA and analysed SLA scales are coherent. On the other hand, an a posteriori check
shows that the correlation radii imposed by the analysis tool are often too great.
The zonal radius is over-estimated by a factor of two, with the exception of the equatorial strip where we can
even see a slight under-estimation around 2°-3° in latitude. The meridianal radius is over-estimated by about
40% north of 20°N and slightly under-estimated between 5°N and 15°S.
We thus intend to adjust the radii for future versions of the analysis/forecasting systems.
There is still some uncertainty as to the correlation structure (function and radii) and this field has to be
investigated further.
It appears that there is a lot of potential for improving the performance of the analysis/forecasting system.
16. The MERCATOR
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- Notebook -
Address
Please send us your comments to the following e-mail address: webmaster@mercator.com.fr
Next issue: January 2003
Editor
Eric Greiner
Authors :
Article 1 : E. Greiner
Article 2 : E. Greiner