Improving the climate data management in the meteorological service of Angola: experience from SASSCAL

Abstract

The knowledge on climate variability in parts of Southern Africa is limited because of the low availability of historic and present-day ground-based observations (Niang et al., 2014). However, there is an increased need of climate information for research, climate adaptation measures and climate services. To respond to the challenges of climate change and related issues, Angola, Botswana, Germany, Namibia, South Africa and Zambia have initiated the interdisciplinary regional competence centre SASSCAL, the "Southern African Science Service Centre for Climate Change and Adaptive Land Management". As part of the initiative, Germany's national meteorological service (Deutscher Wetterdienst, DWD) cooperates with the meteorological services of Angola, Botswana and Zambia in order to improve the management and availability of historical and present-day climate data in these countries. The first results of the cooperation between the German and the Angolan Meteorological Services are presented here. International assessments have shown that improvements of the data management concepts are needed in several countries. The experience of this cooperation can therefore provide hints for comparable activities in other regions.

Adv. Sci. Res., 13, 97105, 2016 www.adv-sci-res.net/13/97/2016/ doi:10.5194/asr-13-97-2016 Author(s) 2016. CC Attribution 3.0 License.

Improving the climate data management in the meteorological service of Angola: experience from SASSCAL

Rafael Posada1, Domingos Nascimento2, Francisco Osvaldo S. Neto2, Jens Riede1, and Frank Kaspar1 1Deutscher Wetterdienst, National Climate Monitoring, Frankfurter Str. 135, 63067 Offenbach, Germany

2Instituto Nacional de Meteorologia e Geosica (INAMET), Luanda, Angola

Correspondence to: Rafael Posada ([email protected])

Received: 15 January 2016 Revised: 20 April 2016 Accepted: 7 June 2016 Published: 20 June 2016

Abstract. The knowledge on climate variability in parts of Southern Africa is limited because of the low availability of historic and present-day ground-based observations (Niang et al., 2014). However, there is an increased need of climate information for research, climate adaptation measures and climate services. To respond to the challenges of climate change and related issues, Angola, Botswana, Germany, Namibia, South Africa and Zambia have initiated the interdisciplinary regional competence centre SASSCAL, the Southern African Science Service Centre for Climate Change and Adaptive Land Management. As part of the initiative, Germanys national meteorological service (Deutscher Wetterdienst, DWD) cooperates with the meteorological services of Angola, Botswana and Zambia in order to improve the management and availability of historical and present-day climate data in these countries. The rst results of the cooperation between the German and the Angolan Meteorological Services are presented here. International assessments have shown that improvements of the data management concepts are needed in several countries. The experience of this cooperation can therefore provide hints for comparable activities in other regions.

1 Introduction

The global climate change issue is stretching the requirements for climate data and data management systems far beyond those originally conceived when the original observational networks were established (WMO, 2011). However, the lack of adequate data and observation systems in Africa seriously hinders the ability of scientists to assess the past and current state of the climate in the region (ACC, 2013). This also applies to parts of Southern Africa, where the knowledge on climate variability is limited because of the low availability of historic and present-day ground-based observations (Niang et al., 2014)

To contribute to reduce this lack of data, the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) initiative supports, among others, activities focused on the installation and reparation of Automatic Weather Stations (AWS) within the SASSCAL region: Angola, Botswana, Namibia, South

Africa and Zambia (Kaspar et al., 2015a). The current1 AWS Network of SASSCAL comprises 132 AWSs.

To meet the expanding needs of climate information for research, climate adaptation measures and climate services it is very important that climate data, both current and historical, are managed in a systematic and comprehensive manner (WMO, 2011).

Climatological data are most useful if they are edited, quality-controlled and stored in a national archive or climate centre and made readily accessible in easy-to-use forms. Although technological innovations are occurring at a rapid pace, many climatological records held by National Meteorological and Hydrological Services (NMHSs) are still in non-digital form. These records must be managed along with the increasing quantity of digital records. A climate data man-

1Status as of 31 March 2016. The data collected by the AWS Network can be accessed online through the website of the SASSCAL WeatherNet: http://www.sasscalweathernet.org

Web End =http://www.sasscalweathernet.org .

15thEMSAnnualMeeting&12thEuropeanConferenceonApplicationsofMeteorology(ECAM)

Published by Copernicus Publications.

98 R. Posada et al.: Improving the climate data management in the meteorological service of Angola

agement system (CDMS) is a set of tools and procedures that allows all data relevant to climate studies to be properly stored and managed (WMO, 2011). A well-constructed CDMS facilitates all the key processes associated with data collection, quality assurance and archival, and is central to the development of all interactive data and information services (GFCS, 2014).

In this context, the SASSCAL initiative supports an activity focused on the improvement of the historical and ongoing climate data management at the NMHS of Angola, Botswana, Namibia and Zambia in cooperation with Germanys national meteorological service (Deutscher Wetter-dienst, DWD). However, the NMHS that actually showed interest in cooperating with the DWD for improving their data management were the Instituto Nacional de Meteorologa e Geofsica of Angola (INAMET), the Department of Meteorological Services of Botswana (DMS) and the Zambia Meteorological Department (ZMD).

An overview of the status and rst results of the cooperation between the DWD and INAMET is presented here.

2 Management of historical and current climate data

The cooperation between the NMHSs is focused on improving the management of climate data in each country in order to make them available for scientic use and for decision makers and diverse stakeholders. More specically, the cooperation focuses on:

implementing a Climate Data Management System (CDMS);

developing operational concepts for the CDMS;

archiving current meteorological observations;

collecting and archiving of already digitized historic climate data;

digitizing and archiving of historic climate data;

capacity building on Climate Data Management.

The core of the cooperation is to implement a reliable CDMS; an integrated computer-based system that facilitates the effective archival, management, analysis, delivery and utilization of a wide range of integrated climate data (WMO, 2014).

During the last decades different CDMS with diverse development approaches have been in use in developing countries (see Stuber et al., 2011), and the delegates of the NMHSs of Angola, Botswana, Zambia and Germany had the opportunity to discuss the different options available during a SASSCAL Workshop held in Namibia in April 2014. At the end of the Workshop it was agreed by all the participants that CLIMSOFT (CLIMatic SOFTware) is the preferred

option, considering that all countries had used this software at some point (Hnsler, 2014).

CLIMSOFT was rst developed by an African team of 3 developers located in Zimbabwe (namely Albert Mhanda), Kenya (Samuel Machua) and Guinea (Barry Aziz) to provide a free and easy-to-use CDMS for developing countries (Stuber et al., 2011). As described in Kaspar et al. (2015a), it has an intuitive Graphical User Interface with a key-entry module, quality control procedures and data import options which allow the import of data from various sources, including data from automatic weather stations. The software is currently based largely on the data base management system (DBMS) Microsoft Access and Microsoft Visual Basic 6, but modications to the software, and a switch to open-source database systems are currently in preparation.One of the most important features of the CLIMSOFT software is its free availability and the fact that it is becoming supported by a large community of developers. The next version of CLIMSOFT is being developed based on the Climate Data Management System Specications of the WMO (WMO, 2014), so that most of the required components are included in the software.

3 Cooperation between INAMET and DWD

3.1 Development of a new data ow at INAMET

The contact between the DWD and INAMET began in 2013 under the SASSCAL framework but it was in April 2014 when an evaluation of the facilities and resources concerning climate data management was accomplished. At that time, the Angolan NMHS did not have an operational CDMS2 and the records of manual weather stations were key-entered in Microsoft Excel (hereafter MS-Excel) tables. These tables were saved in les with different nomenclature at isolated PCs, which made it difcult to identify which data were already digitized and where they were located. Furthermore, an up-to-date inventory of the weather station network was not available, and a unique identier was not assigned to each station. Data recorded by AWS were automatically sent to a server located at INAMET in ASCII format (text les). The need of an improved documentation in data management procedures was also acknowledged.

After recognizing the needs at INAMET, a data ow structure was designed to avoid inconsistencies in the storage of climate data (see Fig. 1). As a rst step, a unique local identier was assigned to each weather station operated by INAMET. For manual weather stations, the identier was assigned based on an on-paper inventory which was available at the headquarters whereas for AWS, a unique number was newly assigned to each station.

2CLIMSOFT was installed once in 2007 but it never became operational due to difculties in its management.

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R. Posada et al.: Improving the climate data management in the meteorological service of Angola 99

Table 1. Overview of Angolan data imported in the CLIMSOFT database from the MS-Excel sheets.Dataset Time Element available Number of Date of Date of Max. number resolution stations rst record last record of records

Daily precipitation 11 2 Jan 1992 31 Dec 2014 1340 Pressure at station level 2 1 Mar 2005 31 Dec 2011 203 Relative humidity 2 1 Mar 2005 31 Dec 2011 234 Maximum temperature 4 1 Jan 1948 5 Jan 2014 333 Minimum temperature 3 1 Jan 1948 31 Dec 2011 230 Mean temperature 1 1 Mar 2011 31 Dec 2011 225 Visibility 1 1 Mar 2011 31 Dec 2011 180

INAMET

Daily

Monthly

Evaporation 9 1 Jan 1952 1 Jun 1975 243 Monthly precipitation 16 1 Jan 1951 1 Jul 2012 584 Pressure at station level 11 1 Jan 1951 1 Dec 2012 616 Relative humidity 14 1 Jan 1951 1 Dec 2012 598 Sunshine duration 7 1 Jan 1952 1 Nov 1991 416 Mean temperature 14 1 Jan 1951 1 Dec 2012 640 Maximum temperature 17 1 Jan 1951 1 Dec 2012 640 Minimum temperature 14 1 Jan 1951 1 Dec 2012 640

Figure 1. Current dataow at INAMET: from the key entry of data in MS-Excel tables at the Data Centre to the import of the data into CLIMSOFT through R.

According to the agreement achieved during the SASSCAL Workshop in April 2014, the CLIMSOFT v3.2 was installed to be used as the CDMS in which meteorological data from both, manual and automatic weather stations are to be stored. The software includes features to key-enter historical data directly into the database. However, it was agreed with the Technical Directorate of INAMET to take advantage of the experience of the personnel in MS-Excel and keep this software as the key-entry system for historical and current data recorded at manual weather stations. Table templates have been created based on the original structure of the MS-Excel tables used previously at INAMET. These templates ensure that the entered data are saved in MS-Excel les with

the identical structure. The MS-Excel les with data entered before the new data management strategy was implemented have been re-formatted to match the format of the table templates.

These les are then saved in a server instead of in isolated computers using a standardized nomenclature. The technicians, responsible for the import of data and maintenance of the CDMS, can access the data directly from the server and proceed with the import into CLIMSOFT. For this to be done, a format conversion of the MS-Excel les is needed, since CLIMSOFT requires the data to be saved in comma-separated values (.csv) les. An easy-to-use routine called conversao e importaao has been developed specically for INAMET to facilitate the conversion and later import of data. This application programmed with R (R Core Team, 2015), guides the user through the conversion and import of data following three steps: (1) import of station metadata; (2) conversion of MS-Excel les into .csv les; and (3) import the data into CLIMSOFT v3.2 (see Fig. 2). The routine is run once a week to update the database with new key-entered data. The key-entered data available in the database are listed in Table 1.

Concerning the data recorded by AWS, CLIMSOFT provides a tool to import them automatically into the database. This tool is being used to import the data of the AWSs installed by SASSCAL and operated by INAMET. The tool checks every two hours whether there are new records from the AWS and, if so, they are entered automatically into the database.

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100 R. Posada et al.: Improving the climate data management in the meteorological service of Angola

Figure 2. User interface of the conversao e importaao developed specically for INAMET.

3.2 Quality control

CLIMSOFT provides a number of quality control (QC) checks that are being used at INAMET to control the quality of the data stored in the main database. They include:

Limits check (based on standard deviation). It checks which observed values of a given element are above (below) a specic upper (lower) threshold. The thresholds are calculated based on the monthly mean of that element plus (minus) n times the standard deviation.

Absolute limits check. It checks which observed values of a given element are above (below) a specic upper (lower) threshold. The thresholds are dened by the user in CLIMSOFT and can be modied according to the climate pattern of the country.

Inter-element comparison. It compares (a) maximum temperature against minimum temperature; (b) dry bulb temperature against wet bulb temperature; (c) maximum temperature against dry bulb temperature; and (d) dry bulb temperature and dew point temperature.

Daily range. It checks if the daily differences between maximum and minimum temperature are above (below) the mean long-term difference plus (minus) n times the standard deviation.

Consecutive days consistency check. It checks if the difference of observed values between consecutive days are above (below) the monthly mean difference plus (minus) n times the standard deviation.

Consecutive hours consistency check. It checks if the difference of observed values in consecutive hours is

above (below) the monthly mean plus (minus) n times the standard deviation.

Besides the QC checks provided by CLIMSOFT, an easy-touse tool has been developed with R to carry out a visual QC of the data stored in CLIMSOFT. This tool allows the user to create a variety of plots (time-series, histogram and wind roses) to facilitate the identication of inconsistencies within the datasets. For instance, time series plots led to the identication of rapid changes exceeding a specied threshold or the detection of gaps within a dataset, as shown in Fig. 3.

The interactivity of these graphics is aimed to allow the user get information from the dataset by navigating through the graphic. These graphical products are being developed as part of a collaborative development project called ClimateObject; an R-package that aims to create a great number of graphical products based on climate data. More information about this project can be found in GitHub, a code-hosting repository based on the GIT version control system (Dabbish et al., 2012): https://github.com/StatisticalServicesCentre/ClimateObject

Web End =https://github.com/StatisticalServicesCentre/ https://github.com/StatisticalServicesCentre/ClimateObject

Web End =ClimateObject . These new features are operational at INAMET since January 2016.

3.3 Collection of historical data

Another task carried out as part of the cooperation is the collection of historical and current data from Angola that are stored in international archives. Up to date, a total of seven different datasets with digitized meteorological data from Angola were identied (see Table 2).

The data were either downloaded from or directly requested from these sources. These data are being stored in a separate Microsoft Access database until they are checked against those data stored at the INAMET database. In this manner it is ensured that there are no inconsistencies in the main database of the NMHS. This second database is also accessible with CLIMSOFT.

More meteorological data from Angola might still exist in non-digital form in archives of other meteorological services, such as in the DWD (Kaspar et al., 2015b), which could complement the dataset already available at INAMET.

Besides the collection of data, INAMET and the Global Precipitation Climate Centre (GPCC) have come to an agreement by which INAMET approves supplying additional daily precipitation data from the weather stations to contribute to the global gridded precipitation analyses carried out by the GPCC (Becker et al., 2013). This will contribute to substantially improve the data coverage and consequently the reliability of the GPCC precipitation analyses across Angola.

3.4 Capacity building

An important aspect of SASSCAL is its educative nature.

Having the capacity building activities as one of the main objectives of the initiative, the cooperation between DWD

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R. Posada et al.: Improving the climate data management in the meteorological service of Angola 101

DatasetTimeSourceElementavailableNumberofDateofDateofMax.number

resolutionstationsrstrecordlatestrecordofrecords

Maximumtemperature192Oct200121Mar2016695

(SYNOP)Minimumtemperature223Dec199822Mar20161226

Dailyprecipitation2211May199822Mar20162138

Meantemperature171Jan19491Feb2014428

Dayswithprecipitation161Jan19681Feb2014199

Monthlyprecipitation171Jan19491Feb2014429

Sealevelpressure71Jan19491Feb2014426

Watervaporpressure161Jan19681Feb2014195

Relativehumidity141Jan19491Nov1967227

MonthlyGeopotential91Jan19561Apr1985202

(CLIMAT)Sunshineduration31Jan19801Nov20132

Quintile111Jan19571Mar1985288

Pressureatthestation51Feb19851Feb201435

Minimummeantemperature11Aug20101Feb201434

Absoluteminimumtemperature11Aug20101Feb201434

Maximummeantemperature11Aug20101Feb201433

Absolutemaximumtemperature11Aug20101Feb201434

GPCCMonthlyDatarequestedbyINAMETMonthlyPrecipitation1981Jan19011Dec20061268

Maximumtemperature628Jan195520Feb20162774

Minimumtemperature628Jan195518Feb20163284

Dailyprecipitation628Jan195515Feb20163165

Lawrimoreetal.(2011a)Meantemperature171Jan19011Feb20161033

PetersonandVose(1997a)Absoluteminimumtemperature171Jan19011Feb20161033

Monthlyprecipitation1041Feb18791Mar19921271

Observedtemperature164Jan195519Oct20146594

Dewpointtemperature164Jan195519Oct20146424

Sealevelpressure43Sep195725Sep20133269

Pressureatthestation34Jan195531Oct19621079

Meanvisibility154Jan195519Oct20146566

Windspeed164Jan195519Oct20146594

Maximumwindspeed164Jan195519Oct20146532

Windstrength220Oct200127Sep201412

Meanmaximumtemperature174Jan195519Oct20146582

Meanminimumtemperature174Jan195519Oct20146577

Dailyprecipitation174Jan195515Oct20143613

Monthlyprecipitation921Jan19481May2010637

Potentialevaporation171Jan19481May2010605

Pressureatthestation171Jan19481Jun2010615

Maximummeantemperature451Jan19481Jun2010652

Minimummeantemperature451Jan19481Jun2010644

Observedtemperature451Jan19481Jun2010654

Relativehumidity441Jan19481Jun2010652

Sunshineduration151Jan19481Aug1992416

Table2.OverviewofAngolandataavailabilityininternationaldatasets.

DatarequestedbyINAMET

DatarequestedbyINAMET

Dailyhttp://leutra.geogr.uni-jena.de/sasscalRBIS/metadata/start.php

Web End =http://leutra.geogr.uni-jena.de/sasscalRBIS/metadata/start.php

Monthlyhttp://leutra.geogr.uni-jena.de/sasscalRBIS/metadata/start.php

Web End =http://leutra.geogr.uni-jena.de/sasscalRBIS/metadata/start.php

DailyMenneetal.(2012a,b)

Daily

Monthly

DWD(WMOreports)

GHCN

RBIS

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102 R. Posada et al.: Improving the climate data management in the meteorological service of Angola

Table2.Continued.

ISTIMonthly ftp://ftp.ncdc.noaa.gov/pub/data/globaldatabank/monthly/stage2/colonialera/

Web End =ftp://ftp.ncdc.noaa.gov/pub/data/globaldatabank/monthly/stage2/colonialera/ Maximum mean temperature 6 1 Jan 1879 1 Dec 1973 1080

(Thorneetal.,2011)Minimummeantemperature61Jan18791Dec19731080

DatasetTimeSourceElementavailableNumberofDateofDateofMax.number

resolutionstationsrstrecordlatestrecordofrecords

IDL

CDIAC

Hourly

DailyDatarequestedbyINAMET

HourlyDatarequestedbyINAMET

Sunshineduration11Jan191631Dec194410319

Minimumtemperature31Jan188031Dec194614610

Maximumtemperature31Jan188031Dec194614609

Maximumirradiationtemperature11Jan191631Dec194410575

Minimumirradiationtemperature17Jan192531Dec19395099

Windspeed11Oct188231Dec194612842

Evaporation11Jan188031Dec18872919

Evaporation(99h)31Jan191531Dec194611675

Grassmaximumtemperature11Jan191631Dec19449568

Grassminimumtemperature11Jan191631Dec194410549

Percentageofinsolation11Jan194531Dec1946730

Totalinsolation024h11Jan194531Dec1946729

Ozone11Jan188031Dec18872918

Dailyprecipitation11Jan188031Dec18872907

Dailyprecipitation(99h)31Jan191531Dec194611649

Precipitationhours(99h)115Aug191931Dec19443009

Soiltemperatureat0.5m11Jan191631Dec194410579

Soiltemperatureat0.8m11Oct191631Dec194410315

Soiltemperatureat1.0m11Oct191631Dec194410298

Soiltemperatureat1.5m11Jan191631Dec194410578

Visibility11Jan194531Dec1946730

Winddirection31Jan188031Dec194641666

Windspeed31Jan188031Dec194642087

Observedtemperature31Jan188031Dec194643822

Soiltemperatureat0m11Jan19215Jan19215

Soiltemperatureat0.3m11Oct191631Dec19201551

Pressureatthestation31Jan191531Dec194635059

Relativehumidty31Jan188031Dec194643821

Actinometry11Jan191731Dec194430067

Totalcloudcover31Jan188031Dec194643822

Watervaporpressure31Jan188031Dec194441631

Sealevelpressure11Jan188031Dec18878761

Windspeed311Jan197128Dec19968807

ftp://cdiac.esd.ornl.gov/pub/ndp026c/

Web End =ftp://cdiac.esd.ornl.gov/pub/ndp026c/ Winddirection311Jan197117Nov19968519

(HahnandWarren,2009)Observedtemperature311Jan197128Dec19968906

Dewpointtemperature311Jan197128Dec19969116

Sealevelpressure251Jan197128Dec19968883

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R. Posada et al.: Improving the climate data management in the meteorological service of Angola 103

Figure 3. Time series of air temperature in Dambo, Uige (Angola). 10 min measurements made by a SASSCAL AWS (top panel) between6 June 2014 and 17 March 2015. In the bottom panel a zoom of the gap of data, between 23 and 27 December 2014.

and INAMET is highly focused on developing the local capacities in the country. Several training activities have been carried out since April 2014. The rst training activity, focused on CLIMSOFT, took place between 8 and 13 August 2014 and was addressed to the local staff of INAMET.In March 2015 the training activities were extended to other institutions and focused on clarifying basic concepts related to climate and climate data management. Besides the local staff of INAMET, representatives of the Fire-Department, as well as of the Food Security Cabinet were taking part of the training. The aim was to improve the awareness on the importance of good practices for the management of climate data. For this, concepts such as metadata and homogeniza-

tion were explained, and the importance of rescuing historical data was stressed (see Fig. 4)

Both, in August 2014 and in March 2015 training in R has been given to the INAMET technicians. This is of special relevance, since proper skills in R programming will allow the technicians to properly understand and manage the software developed to convert and import climate data as well as to create graphical products (see Sects. 2.1 and 2.2). The nal aim is to improve their programming skills so that they can create their own applications according to INAMET needs.

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Figure 4. Photo of the training on Climate Data Management at INAMET in March 2015.

4 Conclusions

Efforts have been done within SASSCAL to improve the availability of climate data in Southern Africa. Besides the installation and repair of AWSs, activities focused on the improvement in the management of historical and current climate data are being carried out in the NMHSs of the region in cooperation with the DWD. The experience described here shows the steps done at INAMET in this matter. A new data ow scheme has been developed and implemented, and CLIMSOFT v3.2 has been installed to serve as the core of a proper CDMS, allowing the management of historical and ongoing climate data at INAMET. Furthermore, the R programming language has been used to facilitate visual quality controls and navigate through the data stored in the CLIMSOFT database, as well as to allow the migration of data from MS-Excel sheets to the database. Therefore, the R routines developed can be seen as part of the CDMS, since it extends the functionality of CLIMSOFT. The Climate Data Management System Specications of the WMO itself (WMO, 2014) states that a CDMS is not expected to contain all of its functionality within a single software package.

These actions are destined to provide a long-term and sustainable solution for the management of climate data at the NMHS. Although the cooperation between the DWD and INAMET is still on-going, the experience so far has led to acknowledge that:

An evaluation of the human and technical resources at the NMHS is essential to recognize the actual local capacities.

The actions aimed to improve the management of climate data should t the local capacities to ensure their implementation.

A complete overview of the work ow at the NMHS is required prior taking any action concerning climate data management.

The directorate of the NMHS has to agree with the actions proposed and should encourage the personnel to implement them.

It is important that the personnel see the advantages of implementing a new system.

The CDMS should take advantage of the local expertise when new tools are developed. For example, at INAMET, it was decided to keep MS-Excel as key-entry system because the staff were very familiar with this software.

Capacity building activities are pillar to make people aware of the importance of having a comprehensive CDMS. At INAMET, basic concepts such as metadata or homogenization were introduced to encourage the employees in charge of the management of climate data to treat the data carefully.

The implementation process should be monitored to assist as soon as any problem arises.

The joint interest of DWD and INAMET is to maintain the cooperation until end 2017 to monitor the improvements made so far and also to take a closer look at other aspects related to climate data management, such as (a) cross-checking Angolan data obtained from international datasets with those stored at INAMET, (b) carrying out quality control of the digitized data stored in the CDMS and (c) locating and organizing on-paper historical records and, if required, supporting data rescue activities for data not yet digitized.

By the end of the cooperation, the improved accessibility of the observational data will allow the application of these data for verication of the national weather forecast run at INAMET. This is of high priority for the NMHS as it will support the provision of more accurate products to end-users and stakeholders, including the scientic community. Understanding predictability and predictive skills of numerical weather prediction models is an important area of research on the weather and seasonal scale (Bauer et al., 2015).

5 Data availability

Some of the international datasets described in Table 2 provide on-line access to Angolan data. This is the case of the Global Historical Climatology Network (GHCN), the River Basin Information System for SASSCAL (RBIS), the International Surface Temperature Initiative (ISTI) and the Carbon Dioxide Information Analysis Center (CDIAC). The links and references to these datasets are provided in the table. The other sources mentioned in Table 2 (i.e. the German

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R. Posada et al.: Improving the climate data management in the meteorological service of Angola 105

Meteorological Service, DWD, the Global Precipitation Climate Centre, GPCC; and the Instituto Dom Luiz, IDL) provided the data to INAMET under request.

Acknowledgements. DWDs contribution to SASSCAL is funded by Germanys Ministry for Education and Research (BMBF, grant no. 01LG1201J). We thank Maria Antnia Valente (Instituto Dom Luiz, Lisbon, Portugal) for the provision of digitized climate observations.

Edited by: I. AuerReviewed by: O. Baddour and one anonymous referee

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