Joint report. Overview of data homogenization procedures

Transcription

1 Joint report Overview of data homogenization procedures Zita Bihari, Tamás Kovács Hungarian Meteorological Service January

2 Project acronym: Project title: Contract number: DELIVERABLE SUMMARY PROJECT INFORMATION DMCSEE Drought Management Centre for South East Europe SEE/A/091/2.2/X Starting date: Ending date: Project WEB site address: Lead partner organisation: Name of representative: Project manager: Environmental Agency of the Republic of Slovenia dr. Silvo Žlebir, director dr. Gregor Gregorič Telephone number: +386 (0) Title of the deliverable: WP/activity related to the deliverable: Type (internal or restricted or public): Location (if relevant): WP leader: Activity leader: Participating partner(s): Author: DELIVERABLE INFORMATION Overview of data homogenization procedures WP3, Activity Internal N/A OMSZ OMSZ OMSZ, NIMH, AUA, GEORAMA, DHMZ, RHMSS, HI-M, HMS, INEUM Zita Bihari, Tamás Kovács Telephone number: Contractual date of delivery to the JTS: Actual date of delivery to the JTS: DELIVERY DEADLINES

3 TABLE OF CONTENTS 1. INTRODUCTION CURRENT STATUS OF HOMOGENIZATION PROCEDURES HOMOGENIZATION METHODS THE PROCESS OF HOMOGENIZATION HOMOGENIZED DATA SERIES METADATA TREATMENT OF HOMOGENIZED DATA SERIES R&D STATUS EFFORTS UNDERTAKEN IN ORDER TO IMPLEMENT CERTAIN HOMOGENIZATION METHOD PLAN TO HOMOGENIZE DATA SERIES REQUEST FOR PERSONAL TRAINING CONCLUSIONS

4 ABSTRACT In Work Package 3 of DMCSEE project a mapping system as a precondition for the establishment of a common methodology for drought assessment has to be developed. The aim of the Act 3.1 is the preparation of climate data and maps. Outcomes of this Act 3.1 are three overviews about climatological databases (Act 3.1.1), procedures used for data quality and homogenisation (Act 3.1.2) and mapping procedures (Act 3.1.3). In recent overview status of data homogenization procedures in partner institutes is described. We present the applied homogenization methods, the homogenized data series and the plans for future developments. Contact address in Hungary: bihari.z@met.hu, lakatos.m@met.hu 4

5 1. INTRODUCTION One of the most important aims of DMCSEE project is mapping of drought. When somebody wants to interpolate drought indices he need: - good quality time series of various climate parameters - reliable interpolation methods In this overview homogenization methods applied by DMCSEE project partners are presented. To facilitate the work of partners a questionnaire was prepared. It was fulfilled by those partners who are climate data holders, namely the national meteorological services. This overview is based on their work. These partners are the following: Environmental Agency of Slovenia, Slovenia (EARS) Hungarian Meteorological Service, Hungary (OMSZ) National Institute of Meteorology and Hydrology, Bulgaria (NIMH) Agricultural University of Athens and Hellenic National Meteorological Service, Greece (AUA-HNMS) Meteorological and Hydrological Service, Croatia (DHMZ) Republic Hydrometeorological Service of Serbia, Serbia (RHMSS) Hydrometeorological Institute of Montenegro, Montenegro (HI-M) Hydrometeorological Service, Republic of Macedonia (HMS) Institute for Energy, Water and Environment, Albania (INEUM) In the overview we tried to give a detailed description about the status of mapping procedures in partner institutes. However if anyone has questions about different parts of the overview, the following contact persons can answer them: 5

6 Contact persons: Institute Country Name(s) (s) EARS Slovenia Gregor Gregorič Mojca Dolinar Gregor Vertačnik OMSZ Hungary Zita Bihari Monika Lakatos NIMH Bulgaria Vesselin Alexandrov AUA HNMS Greece Christos Karavitis Stavros Alexandris Dimitris Stamatakos Dimitris Tsesmelis Vassilia Fassouli Artemis Papapetrou DHMZ Croatia RHMSS Serbia Tatjana Savid Predrag Petrovid HI-M Montenegro Mirjana Ivanov Gordana Markovic Vera Andrijasevic HMS Republic of Macedonia Nina Aleksovska INEUM Albania Liri Muçaj 6

7 2. CURRENT STATUS OF HOMOGENIZATION PROCEDURES Availability of homogenized data series: Homogenized data series are available: EARS, OMSZ, NIHM, DHMZ, INEUM. 2.1 Homogenization methods Used methods: EARS: The Craddock method was used to homogenize some temperature and precipitation series (relative method). OMSZ: Mash method (relative method). NIMH: The Meteo-France, The HU weather service, The Canadian RHtestv2 (relative methods). INEUM: Man test, Double mass curve. Suitability of the methods: For quality control of original data For completion of missing values EARS Generally suitable Generally suitable OMSZ Suitable Suitable NIMH Partly suitable (e.g. The Meteo-France method) Partly suitable (e.g. The Meteo-France method) INEUM Not suitable Not suitable 2.2 The process of homogenization Practice with homogenization: EARS: Not performed regularly, it is based on request. OMSZ: Once in a year, in the beginning of each year NIMH: Up to now once. INEUM: Once. Location of the process: EARS: Outside the data base. Homogenized data series are kept separately. OMSZ: Outside the database. NIMH: Outside the database. INEUM: Inside the database. 7

8 2.3 Homogenized data series Institute Type of the element Temporal resolution of data series Approximate rate of missing data in original series Length of data series Number of stations EARS Temperature Monthly/daily 8 % Few EARS Max. snow cover depth Annual EARS Precipitation Monthly 4 % Few OMSZ Temperature Daily 110 years 15 OMSZ Precipitation Daily 110 years 58 OMSZ Temperature Monthly 40 years 57 OMSZ Precipitation Monthly 60 years 177 OMSZ Rel. humidity Monthly 30 yeras OMSZ Wind speed Monthly 30 years OMSZ Cloudiness Monthly 30 years NIMH Temperature Monthly <30% years 40 NIMH Precipitation Monthly <30% years 60 NIMH Sunshine Monthly <30% 50 years 10 duration DHMZ Temperature Annual 1% 50 years 20 DHMZ Precipitation Annual 1% 50 years 30 INEUM Temperature Monthly 3% 40 years 62 INEUM Precipitation Monthly 4% 40 years Metadata EARS, OMSZ, DHMZ, INEUM: The homogenization method uses metadata as input. NIMH: Metadata not used. 8

9 2.5 Treatment of homogenized data series Storage of data series: EARS: Homogenized data series are stored outside the normal climatological data base. Both the original and homogenized series are stored. OMSZ: Homogenized data series are stored outside the data base. Both the original and homogenized series are stored. NIMH: Homogenized data series are stored both inside and outside the data base. Both the original and homogenized series are stored. DHMZ: Both the original and homogenized series are stored. INEUM: Both the original and homogenized series are stored. Purpose of homogenized data series: EARS: Climate studies. Main purpose is to prepare a homogenized data set for trend calculations since it is frequently required to prepare homogenized data series OMSZ: Climate studies. NIMH: So far research including climate studies DHMZ: Research and climate applications INEUM: Climate studies. 9

10 3. R&D STATUS EFFORTS UNDERTAKEN IN ORDER TO IMPLEMENT CERTAIN HOMOGENIZATION METHOD 3.1. Plan to homogenize data series EARS: The feasibility of the Craddock method is being studied for operational temperature series homogenization and (in test mode) also precipitation data series. OMSZ: No. NIMH: Yes. AUA-HNMS: Yes, DHMZ: RHMSS: Yes. HI-M: Yes. HMS: We are working on the program MASH from March INEUM: No. Institute Details of demands: Type of the element Temporal resolution of data series 10 Approx. rate of missing data in original series Length of data series Nr. of stations EARS Temperature Monthly? 20 a 50 a+ > 100 EARS Precipitation Monthly? 20 a 50 a+ > 300 EARS Sunshine duration Monthly? 25 a + > 50 EARS Solar irradiance Hourly Few percent 5-17 years > 20 EARS Snow depth Monthly Few percent 20 a-50 a+ > 300 NIMH Temperature Monthly <30% years 40 NIMH Precipitation Monthly <30% years 60 NIMH Sunshine duration Monthly <30% 50 years 10 AUA-HNMS Temperature Daily 20% in monthly series 45 years AUA-HNMS Precipitation Monthly 20% 45 years AUA-HNMS Relative humidity Monthly 20% 45 years AUA-HNMS Wind Monthly 20% 45 years RHMSS Temperature Annual/monthly 5% present 55 RHMSS Precipitation Annual/monthly 5% present 55 HI-M Temperature Monthly 60 years 10 HI-M Precipitation Monthly 60 years 10-up

11 3.2. Request for personal training EARS: Not at the moment. OMSZ: No. NIMH: Although NIMH can do data homogenization by itself, any help in the field would be useful. AUA-HNMS: Mash is already used, they are in process of homogenizing monthly temperature series from 5 stations in Eastern Aegean area. Help is definitely needed with Mash in order to homogenize precipitaion and wind. RHMSS: Yes. HI-M: Yes. HMS: Yes. INEUM: No. 11

12 4. CONCLUSIONS The final aim of application adequate homogenized data series in the DMCSEE project is to make regional maps of drought indices. While the drought index calculations are based on long time data series, use of homogenized data is very important. However the half of partners don t have homogenized data series unfortunately. While the SPI index is based only on monthly precipitation sum, homogenization of the necessary input precipitation data can be solved in a simple way. In the frame of the project OMSZ can give personal training to the partners who need help in homogenization. The experts of OMSZ can travel to the parner s institute and help in the homogenization of their own data. The organization of travel began with HI-M but OMSZ is waiting for the request of other partners. 12