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1 OLADE Risk Management Course Module 1 Climate databases 1

Cristian Perez

Academic year: 2022

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Module 1: Climate data-bases. Part 1.

Prepared by Jorge Paz ( Tecnalia)


1.- Presentation of this module

Main objectives

• To determine the climate data that are needed for engineering practice.

• To understand the different sources of climate data available

• To acquire climate data applicable to engineering modelling

Operational objectives:

• Differentiate between types of measurements such as direct and indirect, and remote and in-situ measurements

• Identify different types of meteorological observing systems and their observational representativeness including temporal and spatial scales

• Describe instrument and measurement uncertainty

• Understand the factors that are used to assess systematic and random errors, and the propagation of errors.

Recommended resources:

• A computer.

• Spreadsheet software: Microsoft Excel, but Openoffice and other open software is always welcome!!

• A flexible text editor: NotePad++ is an example (Windows Notes isn´t).

• Python: Anaconda platform recommended.

• A compression program: IzArc, etc.

• Google Chrome and a gmail account.


1.- Presentation of this module

Additional materials:

Copernicus Climate Change User Learning Service (https://uls.climate.copernicus.eu/). It is particularly interesting the lessons on observations: https://uls.climate.copernicus.eu/group/learning/browse-lessons?packageId=1157

MOOCs from Copernicus:




WMO. The main page of interest is: “Observation components of the Global Observing System”:


NOAA Data access: https://www.ncdc.noaa.gov/data-access. In addition to data for the USA you will find here a description of the different kinds of climate information.

Eumetrain (www.eumetrain.org), maybe too specialized if you don´t have a particular interest.

The Comet program (http://www.comet.ucar.edu/). Of special interest the free collection of hundreds of training resources (https://www.meted.ucar.edu/index.php), the COMET Training Portfolio (https://www.comet.ucar.edu/portfolio/index.htm), learning resources (https://www.meted.ucar.edu/training_detail.php) and particularly: Introduction to tropical meteorology (2ndedition), Chapter 9 - Observation, Analysis, and Prediction (http://kejian1.cmatc.cn/vod/comet/tropical/textbook_2nd_edition/print_9.htm).

ClimateEurope H2020 project: https://www.climateurope.eu/. Review the summary of data sources:


We will not explain the operation of the different instruments, so please take a look to:

New world climate: Weather Instruments and Equipment Explained: http://www.nwclimate.org/guides/meteorological- instrumentation/

Sciencing: Weather Instruments & Their Uses:https://sciencing.com/weather-instruments-uses-8013246.html

NOTE: if not indicated, all links accessed on December 2020. Contact me if they don´t work or if you are interested in any particular topic.


1.- Presentation of this module

Contents related to climate data:

1.- Presentation of this module 2.- Introduction to climate 3.- Sources of climate data 4.- Types of observations 5.- Land stations

6.- Ocean in-situ observations 7.- Sondes and airplanes 8.- Radar and Lidar 9.- Satellites

10.- Historical observations 11.- River flow

12.- Homogenization 13.- Climate data sources 14.- Python exercise

Part 1: Numerical Modelling in Energy Sytems

Part 2: Numerical Modelling in Environmental Processes

Python exercise


Definition of “climate”:

Cambridge dictionary: the general weather conditions usually found in a particular place

WMO: Climate describes the average weather conditions for a particular location and over a long period of time. We study the climate, its variations and extremes, and its influences on a variety of activities including human health, safety and welfare to support evidence-based decision-making on how to best adapt to a changing climate.

Source: WMO: https://public.wmo.int/en/our- mandate/climate

IPCC: Climate in a narrow sense is usually defined as the average weather, or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization. The relevant quantities are most often surface variables such as temperature, precipitation and wind.

Climate in a wider sense is the state, including a statistical description, of the climate system.

Source: IPCC:


More information about the IPCC: www.ipcc.ch

More information about the WMO: https://public.wmo.int/en

2.- Introduction to climate

Question: Is the IPCC the best research institution on climate change? Do it have other objectives? How many assessment reports have been published? Do they have clear rules for communicating uncertainty?


Definition of Essential Climate Variables

The Global Climate Observing System (GCOS), part of the WMO, has defined more than 50 Essential Climate Variables (ECVs = a physical, chemical, or biological variable or a group of linked variables that critically contributes to the characterization of Earth’s climate). These include atmospheric variables (temperature, moisture, radiation, and clouds), but also biosphere, oceans, anthroposphere, etc.

Source: [1]


[1] ECV: https://gcos.wmo.int/en/essential-climate-variables

[2] GCOS: https://public.wmo.int/en/programmes/global-climate-observing-system

2.- Introduction to climate

Take a look to two/three ECVs of your interest. Do you find any information about in which latitudes are increasing the temperatures more? What is the name of this phenomenon?


Definition of our own requisites

ECV focus the efforts of Earth Observation system, but for engineering practice, it is important to evaluate the specific requirements determined by the legal requisite to be met, the technical procedure or standard to be applied, the software of model that is intended to be used, etc. Important topics to be determined are:

- The specific location where the variable is measured: coordinates of the monitoring point vs our point of interest, indoor /outdoor, height above the surface, height above the sea level, etc.

- The specific characteristics of the variable: diffuse radiation vs direct radiation, total precipitation vs snow/rain/etc.

- Period for integration: daily values vs hourly, 3s wind guts vs 10 min means, only one representative value (a percentile, a return period,…) etc.

- Temporal and spatial resolution.

- Longitude of the times series needed (30 year, 1 synthetic year, one representative day, etc.)

- Time horizon (now, past, next months, next years, etc.)

Sometimes, obtaining the variable that we need require some operations and the combination of different variables. Source of the picture: IKEA

Think on a particular study, challenge, case, etc. you would like to face during the course and define the points listed on the left.

2.- Introduction to climate


Sources of climate information: Observations vs models

There are various data resources for atmospheric climate variables and the period for which they provide data, that can be split in observations (providing information for the past) and models (which additionally to past records, are also able to provide forecasts and projections for the future).

Climate models Observations

Now 2-4 weeks 6-7 months

Weather forecasts

Seasonal & subseasonal forecasts

Decadal predictions

Multidecadal projections

10-20 years 100 years


3.- Sources of climate data


Types of observations

Besides the traditional observation stations on land, currently there are many more direct or indirect observation methods as can be seen in the figure:

Historical observations (ice cores, tree rings, corals, old shipping reports etc.)

Observations made at weather stations on land (max 170 years)

Land radiosondes

Observations made at sea by ships (surface and radiosondes)


Weather radars

Observations made by aircrafts




Generally, data are centralized in national / regional services:

AEMET: https://opendata.aemet.es/centrodedescargas/inicio

Euskalmet: https://www.euskalmet.euskadi.eus

Meteocat: https://www.meteo.cat/wpweb/climatologia/serveis-i-dades-climatiques/


INAMHI (EC):http://www.inamhi.gob.ec/

SEMANHI (PE):https://www.senamhi.gob.pe/


Source: The COMET Program:


4.- Types of observations


Direct vs indirect observations

Direct observations are observations you can measure directly, for example the temperature or pressure at a land station.

Indirect observations are observations that are derived from other observations. For example, past temperatures can be derived from tree ring or ice cores.

In situ vs remote sensors

In situ sensors only measure their immediate environment.

Remote sensors measure over distances that extend significantly beyond the location of the instrument

Source: AEMET:

http://www.aemet.es/es/idi/ob servacion/observacion_convenci onal


The figure on the botton right shows an automatic station launching a

“radiosonda”, an aerostatic balloon with equipment that measures various atmospheric parameters (temperature, pressure, etc.) and transmits them by radio to the ground station. Is this a remote or an in-situ sensor?

4.- Types of observations


Types of instruments

• Class 1: measure in situ at a point; they occupy a small volume of the phenomena being measured (e.g., air temperature measured by ground station thermometer).

• Class 2: measure area-averaged or volume-averaged variables remotely (e.g., temperature derived from satellite radiance or precipitation derived from radar reflectivity).

• Class 3: measure wind velocity from tracking physical targets and their observed displacement with time (e.g., sondes tracked by Global Positioning Satellites or wind

velocity derived from tracking cloud elements in satellite images). Very unusual. The Meteosat Second Generation spacecraft in orbit with the main payload SEVIRI (image credit: ESA) https://earth.esa.int/web/eoportal/satellite- missions/m/meteosat-second-generation

4.- Types of observations


Describe these instruments as class1/class2/class3, as direct/indirect and as in-situ/remote:


Land stations: global distribution

There are thousands of weather or meteorological stations measuring at or near the Earth’s surface

meteorological parameters such as atmospheric pressure, wind speed and direction, air temperature and relative humidity.

These are observations at one location, or “in situ”. The representativeness of their measurements depends on their specific location.

Location of all stations in the stage 2 components of the International Surface Temperature Initiative (ISTI) databank. The color corresponds to the number of years of data available for each station. Stations with longer periods of record mask stations with shorter periods of record when they are in approximate identical locations. Source: J. J. Rennie et al, 2014: The international surface temperature initiative global land surface databank: monthly temperature data release description and methods.


5.- Observations: Land stations

Land station at Santander (Spain)


Land stations: parameters

The most commonly measured variables are:

- temperature (2 m) - grass temperature (5 cm) - soil temperature - humidity

- precipitation - radiation

- wind speed and direction - clouds

- pressure - visibility

The World Meteorological Organisation (WMO) formulated standards for these

meteorological stations (e.g., temperature is measured at 2 meters height, around the station there should not be high vegetation, etc.).

IoT revolution is ongoing, and there are meteorological sensors in multiple places, facilities (not always representative of surroundings areas…and, usually, with short series)

This presentation summarizes WMO Best practices stablished by https://www.wmo.int/pages/prog/www/OSY/Meetings/GCW- IM1/Doc7.1_BestPractices.pdf

Example of temperature staion including Grass temperatura, Source: Met Office: How we measure temperature


weather/guides/observations/h ow-we-measure-temperature

Amateur meteorological station with sensors for humidity, temperature, wind speed and direction,

precipitation, air pressure.

Source: www.bresser.de

Pyranometer in a photovoltaic installation. Source: wikipedia

5.- Observations: Land stations


Land stations: digital vs analogical data

In developed countries, most stations are digitalized, and their data are provided by Meteorological services in a digital format (generally free, but not always). In many developing countries, the transition to automated observations is still in progress, and the meteorological community still relies on observations taken manually by weather observers and transmitted into the international networks for use in global weather and climate models.

For more info about data rescue, visit:

[1] I-DARE : http://www.wmo.int/pages/prog/wcp/wcdmp/documents/IDARE_wcdmp83.pdf [2] C3S Data Rescue Service: https://datarescue.climate.copernicus.eu/

To be rescued data.

Source: [1]

Old data and manual operated stations have their data “kidnapped “ in analogical formats, and several “rescue” initiatives try to digitalize them.

The aim is to create larger/denser records.

5.- Observations: Land stations

Manual station at Torremocha del Jiloca (Spain) recording it minimun temperature ever: -26,5° on 12/01/20. Source:

https://twitter.com/VicenteAupi/status/1348991 903329607681

Question: a station which data are recorded every day at 15:00 manually is going to be automated.

May this affect the records of maximum and minimum daily temperatures?


Land stations: measuring conditions

To be able to compare and combine measurements between different meteorological stations there are strict criteria for the set up of measurement sites and the

measurement techniques. Measurements are generally done in open areas in the countryside that are representative for the surroundings.

Measurements in cities are difficult, it is hard to find a location where measurements are representative for a larger area. Differences in especially wind and

temperature can be very large over short distances in a city. However, many people live in urban areas, and for example temperature is often somewhat higher in urban areas (Urban Heat Island = UHI). Therefore, there is an increasing number of measuring networks in cities

As aviation demands updated weather, airports are also frequent locations for

meteorological stations. These big infrastructures can present conditions that are not always representative of the conditions in the city.

Additional information: WMO guidelines of meteorological instruments and methods of observation:


Number of frozen days and Mean temperatures from two stations located at Madrid airport (Barajas) and an urban park in the city centre (Retiro). Source:

Ministerio de Transportes, Movilidad y Agenda Urbana, (c2020): Estudio informativo del nuevo complejo ferroviario de la estación de Madrid- Chamartín

https://www.mitma.gob.es/recursos_mfom/paginabasica/recursos/07_clima tologia_hidrologia_y_drenaje.pdf

5.- Observations: Land stations


Gridded observations: European Climate Assessment &

Dataset project and E-OBS

ECA&D is receiving data from 79 participants for 65 countries and the ECA dataset contains 74105 series of observations for 13 elements at 20095 meteorological stations throughout Europe and the Mediterranean (see Daily data > Data dictionary).

E-OBS is a daily gridded observational dataset for precipitation, temperature and sea level pressure in Europe based on ECA&D information. The full dataset covers the period 1950-01-01 until 2020-06-30. It has originally been developed and updated as parts of the ENSEMBLES (EU-FP6), EURO4M (EU- FP7) and UERRA (EU-FP7) projects. Currently it is maintained and elaborated as part of the Copernicus Climate Change Services.

Additional information:

[1] ECA&D: https://www.ecad.eu/

[2] ECA&D stations: https://knmi-ecad-assets-

prd.s3.amazonaws.com/download/stations.txt( a good example of human and machine readable file.

[3] C3S: E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations: https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu- gridded-observations-europe?tab=overview

Maximum temperature in Europe on 4 August 2003.


https://www.ecad.eu/maxte mp_EOBS.php

5.- Observations: Land stations

Variables of E-OBS at Climate Data Store of the C3S: Source: [3]


University of East Anglia Climate Research Unit:

This authoritative source provides different kinds of instrumental data:

-Temperature(CRUTEM4, HadCRUT4: 1850-present global data on a 5° x 5° grid): HadCRUT is a global temperature dataset, providing gridded temperature anomalies across the world as well as averages for the hemispheres and the globe as a whole. CRUTEM and HadSST are temperature datasets for the land and ocean regions, respectively, and contribute to the global dataset.

- High-resolution gridded datasets (CRU TS, CRU CY, CRU CL: 1901-2016 global land data for multiple variables on a 0.5° x 0.5° or finer grid)

- Precipitation and drought:

scPDSI drought indices for Global land, Europe and Alps

Precipitation(Hulme dataset: global land data on 5°x5° and 2.5°x3.75° grids for 1900-1998) - Pressure and Circulation Indices

Pressure data, NAO, SOI and other circulation indices, etc.

UK Climate Indicesfor Lamb Weather Types

- Climate data for individual locations, countries and regions

It also provides, Paleoclimate (tree rings data), Reanalysis, and climate models (future projections).

Additional information:

[1] University of East Anglia Climate Research Unit: https://sites.uea.ac.uk/cru/data/

[2] UEA Climate Research Unit: temperature datasets: https://crudata.uea.ac.uk/cru/data/temperature/

Temperature anomalies for the whole world calculated from HadCRUT4 dataset. Source:


5.- Observations: Land stations

We will study all this in next lessons


Other gridded datasets:

5.- Observations: Land stations



TEMPERATURE EXTREMES HadGHCND (aka HadGHCNDEX) is a gridded daily dataset of near-surface maximum (TX) and minimum (TN) temperature observations. Anomalies (departures from 1961-1990 climatology) and actual temperatures (1950/01 to 2011/12) are distributed in two separate files.

Download: https://www.metoffice.gov.uk/hadobs/hadghcnd/

Long record of daily temperature extremes

HadGHCND is regularly updated by NCEI and the Hadley Centre and provides a near-real time update of daily maximum and minimum temperature fields across the globe.

Fewer indices than GHCHNDEX or HadEX2 Sparse coverage in Southern Hemisphere and Tropics


Provides gridded, station-based indices of temperature- and precipitation- related climate extremes. It is intended for climate change detection and attribution studies, climate model evaluation, and operational monitoring of extreme climatic events. Twenty-six indices, including daily maximum and minimum temperatures, number of frost days, maximum 1-day precipitation, and growing season length are provided for 1951 to the present at monthly timesteps on a 2.5°x2.5° grid. Calculated using the r package climdex. Recommended download: Climdex

Large number of indices provided calculated with standard, intuitive definitions of extremes

Operationally updated

Global coverage not as complete as other data sets including more station networks

No homogenization to account for changes in observing practices, instrumentation, site location or related issues.

Uneven updating; e.g. European & North American observations updated more regularly than African and South American observations


HadEX2 provides gridded, station-based indices of temperature- and precipitation- related climate extremes. It is intended for climate change detection and attribution studies and climate model evaluation. Twenty-nine indices, including daily maximum and minimum temperatures, number of frost days, maximum 1-day precipitation, and growing season length are provided for 1901 to 2010 at monthly timesteps on a 2.5° latitude x 3.75 ° longitude grid. Recommended download page: Climdex

Large number of indices provided calculated with standard, intuitive definitions of extremes

Most global coverage of the "extremes" datasets, incorporating stations from Europe, Southeast Asia, and Latin America, in addition to GHCN-Daily

More quality control and homogenization of stations performed than for GHCNDEX

Not routinely updated

Underlying station data not as accessible as the GHCN-Daily stations

HADEX3 is available!


29 indices of temperature and precipitation on a 1.25° x 1.875° grid from 1901 to 2018. The indices represent seasonal and/or annual values derived from daily station data. Data are calculated using the Climpact2 software or have been submitted to the project as pre-calculated indices. NOTE: CLIMPACT 2 is deprecated from 12/2020: use; https://github.com/ARCCSS-extremes/climpactRecommended download page: Climdex

Higher resolution Updated,

Two reference periods: 1961-1990 and 1981-2010


[1] National Center for Atmospheric Research Staff (Eds). Last modified 05 May 2014. "The Climate Data Guide: HadGHCNDEX - Gridded Daily Temperature Extremes."

Retrieved from https://climatedataguide.ucar.edu/climate-data/hadghcndex-gridded-daily-temperature-extremes.

[2] CRAN - Package climdex.pcic: cran.r-project.org

[2] MetOffice: HadEX3: Gridded land surface extremes indices: https://www.metoffice.gov.uk/hadobs/hadex3/

[3] Climpact2: https://github.com/ARCCSS-extremes/climpact2/

[4] ARCCSS-extremes/climpact: https://github.com/ARCCSS-extremes/climpact


Other gridded datasets:

5.- Observations: Land stations



[1] Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.



https://clim-engine-development.appspot.com/fewsNet https://climateserv.servirglobal.net/


Near present Quasi-global

Multiple access channels High resolution


[2] CHIRTS-daily is a global 2-m maximum temperature (Tmax) product that combines the monthly CHIRTSmax data set with the ERA5 reanalysis to produce routinely updated data to support the monitoring of temperature extreme.

Cuasi-global (60°S – 70°N), high-resolution (0.05° x 0.05°, approx. 5km) Download:

Near present Quasi-global

Multiple access channels High resolution

Monthly fields, disaggregated daily using ERA5 Only Tmax and Tmin

CRU TS v. 4.05

[3] The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.05 data are month-by-month variations in climate over the period 1901-2020, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.

Coverage: All land areas (excluding Antarctica) 0.5° resolution

Several variables : pre, tmp, tmx, tmn, dtr, vap, cld, wet, frs, pet --

[1] University of California, Santa Barbara: CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations

[2] University of California, Santa Barbara: CHIRTSdaily

[3] Climatic Research Unit: High-resolution gridded datasets (and derived products)


There are many other gridded historical data. You can take a look to some of them (and reanalysis) in the IPCC WGI Interactive Atlas: Regional information (Advanced)

Can you identify areas with significant trends of precipitation decrease?


Land stations and atmospheric composition

European, national, etc. legislation stablish:

- Objectives and air quality goals for different pollutants - Techniques for monitoring of air quality parameters.

- Number of air quality stations needed

Air quality monitoring stations/networks provides concentrations of NOx; Particullate matter, O3, CO, Bencene, HAP, etc.

….but usually also Temperature, precipitation, relative humidity, wind, solar radiation, etc.

Mainly located in cities, industrial regions, but also in “background” isolated areas. They are operated by regional governments, municipalities, Ministries, etc.

[1] List of all networks in Spain:


[2] Example of Spanish regional network:


[3] Example of Spanish municipal air quality monitoring network: http://www.mambiente.madrid.es/sica/scripts/index.php [4] Background stations:

http://www.aemet.es/es/eltiempo/observacion/contaminacionfondo [5] example paper about correlations between variables:

Amaury Souza et al., 2017: Analysis of the correlations between NO, NO2 and O3 concentrations in Campo Grande – MS, Brazil.

https://www.researchgate.net/publication/318870568_Analysis_of_the_correlations_between_NO_NO2_and_O3_concentrations_in_Campo_Gr ande_-_MS_Brazil

Average concentrations of O3 and UV irradiance. Source: [5]

Air quality monitoring station. Source [3]

5.- Observations: Land stations


Land stations and atmospheric composition

The WMO Global Atmosphere Watch (GAW) project has the goal of “addressing atmospheric composition on all scales: from global and regional to local and urban”. A set of GAW stations measure relatively undisturbed air, at remote locations or near the top of mountains,

providing long series of interest for climate change.

There are also national networks. In Spain “Red de contaminación de fondo”, operated by AEMET).

Monthly (red) and annual (black) CO2 concentrations at Mauna Loa. Source: [4]

GAW stations. Source [2]

5.- Observations: Land stations

Additional information:

[1] GAW: https://public.wmo.int/en/programmes/global- atmosphere-watch-programme

[2] GAW Stations: WMO 2010: The Global Atmosphere Watch: a History of Contributing to Climate Monitoring.

https://public.wmo.int/en/bulletin/global-atmosphere-watch- history-contributing-climate-monitoring

[3] NOAA GAW data access: https://www.ncdc.noaa.gov/gaw- data-access

[4] NOAA: Monthly Average Mauna Loa CO2, https://www.esrl.noaa.gov/gmd/ccgg/trends/

[5] AEMET: Contaminación de fondo:


ndo Spanish background network. Source: [5]


5.- Observations: Land stations

Agro-meteorological networks

Son de especial interés porque son redes paralelas, con sensores generalmente más baratas, operadas por otros agentes listado en [2], orientadas a la investigación / gestión. Etc.

Permiten del desarrollo de interesantes aplicaciones: visores de apoyo a la toma de decisiones de regantes [3]

Additional information:

[1]https://www.researchgate.net/publication/338779501_Agricultural_Meteorology_and_Climatology/figures?lo=1 [2] https://www.mapa.gob.es/es/desarrollo-rural/temas/gestion-sostenible-regadios/sistema-informacion-agroclimatica- regadio/informaciondeinteres.aspx

[3] http://www.inforiego.org/opencms/opencms/visor_inforiego/index.html

Example of a commercia automatic and solar- energy driven agrometeorological weather station for measurement of standard meteorological elements and online data transfer; (Source: BOKU-Met). Source [1]


Land stations: summary

Advantages of land stations:

1. Some land stations measure since the 1850’s, a lot more from the 1950’s . Therefore, long time series are available for these sites.

2. Direct measurement of the Essential Climate Variables (ECV’s) Disadvantages of land stations:

1. They are not evenly distributed along the globe. Especially the southern hemisphere and the arctic regions are underrepresented.

2. In-situ measurements are only representative for a small area around the measurement site. Features like the urban heat island will be missed.

3. Inhomogeneities are present in many time series because of changes to the measurement site or its surroundings

5.- Observations: Land stations


Over the oceans in situ observations

Over the oceans the Global Ocean Observing System (GOOS) relies on:

• ships

• moored and drifting buoys

• stationary platforms

• other systems (satellites, HFR, etc.)

Main in-situ elements of the Global Ocean observing System. Source:


[1] Interactive version of this map: https://www.ocean-ops.org/board

[2] An example of how all this sources are combined to generate climate products: Optimum Interpolation Sea Surface Temperature (OISST) https://www.ncdc.noaa.gov/oisst

[3] Taken from C3S tweet: https://twitter.com/CopernicusEU/status/1347106977202188288. Original source: Melet et al. 2020

6.- Observations: Ocean in-situ observations

Schematic representation of the hazards (normal font) and metocean variables (bold) in the coastal zone. Source: [3]


Observations at sea - ships

Ships measure the same variables as at surface land stations. Observations are made by ships recruited under the WMO Voluntary Observing Ship (VOS) Programme.

They provide information on trajectories, not for one location.

The number of observing ships is around 4 000. About 1 000 of them report observations every day. To increase this percentage, automation is promoted through the The Automated Shipboard Aerological Programme (ASAP).

[1] The Voluntary Observing Program at WMO:


[2] The VOS at NOAA:


[3] Example of private initiative to support the program:

http://www.coastalenvironmental.com/marine-weather-stations.html [4] Example of downloadable data from ships


Tracks of Voluntary Observing Ships Source: [3]

Automatic Voluntary Observing Ships System. Source:

https://axystechnologies.com/solutions/shipboard- weather/

6.- Observations: Ocean in-situ observations


Observations with sea Buoys

Buoys are instruments which collect weather and ocean data within the world's oceans. There are

- moored buoys which remain at the same location and are

generally equipped with complex instruments met stations, wave measurement, etc.

- drifting buoys , which move with the sea currents

The operational drifting buoy programme comprised of about 1 200 drifting buoys provides over 27 000 sea surface temperature

measurements per day. Half of the drifters also report sea level pressure providing about 14 000 reports per day (as reported in 2018).

For more info about buoys, visit:

[1] https://www.whoi.edu/what-we-do/explore/instruments/instruments-sensors- samplers/improved-meteorological-packages-imet/

[2] NOAA: Global Drifter Program . https://www.aoml.noaa.gov/global-drifter- program/

Moored buoy in the Bay of Bengal equipped with an Air- Sea Interaction Meteorology (ASIMET) system. Source [1]

6.- Observations: Ocean in-situ observations

Drifted buoy system.

Source [2]


Observations at sea with gliders and other equipment

Argo is an array of over 3600 profiling floats (as reported in 2018) distributed almost uniformly across the global oceans. They provide temperature and salinity profiles from the surface to a depth of 2000m. Argo provides one of the most accurate and comprehensive means of observing global ocean temperature and salinity changes.

Glidders collecting water temperature, salinity, dissolved oxygen and other parameters are used for monitoring the Gulf of Mexico and other locations.

The boats participating in The Ship of Opportunity Program delivers eXpendable BathyThermograph (XBT), a temperature probe that is launched from the ship. The data

are logged to a computer provided by the NOAA, where it is processed and formatted for satellite transmission in real-time through the Global Telecommunications System (GTS)

For more information:

[1] Argo program: https://argo.ucsd.edu/

[2] The Gulf of Mexico Coastal Ocean Observing System (GCOOS):

[3] https://products.gcoos.org/gliders/

[4] Ship-of-Opportunity Programme (SOOP): https://www.ocean-ops.org/sot/programmes.html#VOS

Operation of an Argo float [1]

Gliders. Source [2]

Position of gliders and Argos in the Gulf of Mexico [3]

6.- Observations: Ocean in-situ observations

Launching of a XBT [4]


Observations at sea: platforms

Stationary platforms are mostly located in the vicinity of the coast, often located on oil or gas platforms. They are comparable to the measurement sites at land (and also to big stationary buoys).

Their data are usually provided with data from buoys (see examples below)

Examples of portals providing buoy/platform data:

NOAA: Data Buoy Centre: https://www.ndbc.noaa.gov/

Puertos del Estado: Predicción de oleaje, nivel del mar ; Boyas y mareógrafos: http://www.puertos.es/es- es/oceanografia/Paginas/portus.aspx

6.- Observations: Ocean in-situ observations

Meteorological equipment on a platform. Source: from left to right, 1 & 2 NOAA, Data Buoy Centre (https://www.ndbc.noaa.gov/images/stations/sanf1.jpg), 3 Australian Government, Bureau of meteorology (http://media.bom.gov.au/social/blog/627/our-offshore-automatic- weather-stations-in-the-coral-sea/)


Mareographs or tide gauges

Measures changes in sea level relative to a datum (a height reference).

Old ones are base on floatability and are analog. The new ones include new

technologies (acoustic, microwaves) that protects the sensors and reduce


Their data are generally provided by national weather services and/or port authorities.


[1] NOAA: What is a tide gauge?.


[2] Wikipedia:

https://en.wikipedia.org/wiki/Tide_gauge#/media/File:CascaisTideGau ge_Interior.jpg CC-BY-SA-4.0

6.- Observations: Ocean in-situ observations

Diagrams of NOAA San Francisco Tide Station after and before upgrading. Source [1]

Mareograph in Santander Source: author

Interior view of the Cascais Tide Gauge showing the data recording device. Source: [2]


Observations at sea: summary


- Important for weather and climate models because oceans cover a large part of the world.

- Direct measurements of the ECV’s

- Wide and uniform spatial distribution (in case of Argo floats) Disadvantages:

- No global coverage of buoys and ships. Ship data are mainly available on the main shipping routes.

Limited coverage at high latitudes, especially in seasonally ice-covered regions.

- Ships and drifting buoys do not have a fixed location - No long time series at fixed locations (Argo since 2000)

6.- Observations: Ocean in-situ observations

Diagram of the Deep-ocean Assessment and Reporting of Tsunamis (DART) system. Some of this buoys also record wind, barometric pressure, sea surface temp and conductivity, atmospheric temperature and Relative Humidity. etc. Source:



Thank you!

Please, don´t hesitate to contact us:

Jorge Paz: jorge.paz@tecnalia.com


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