Damaging Wind Gust Index. Prepared for Ministry for the Environment

Transcription

1 Damaging Wind Gust Index Prepared for Ministry for the Environment March 2017

2 Prepared by: Gregor Macara For any information regarding this report please contact: Gregor Macara Climate Scientist, Climate Data and Applications NIWA National Institute of Water & Atmospheric Research Ltd Private Bag Kilbirnie Wellington 6241 Phone NIWA CLIENT REPORT No: WN Report date: March 2017 NIWA Project: MFE17304 Quality Assurance Statement Reviewed by: Dr Andrew Tait Formatting checked by: P Allen Approved for release by: Dr Andrew Laing All rights reserved. This publication may not be reproduced or copied in any form without the permission of the copyright owner(s). Such permission is only to be given in accordance with the terms of the client s contract with NIWA. This copyright extends to all forms of copying and any storage of material in any kind of information retrieval system. Whilst NIWA has used all reasonable endeavours to ensure that the information contained in this document is accurate, NIWA does not give any express or implied warranty as to the completeness of the information contained herein, or that it will be suitable for any purpose(s) other than those specifically contemplated during the Project or agreed by NIWA and the Client.

3 Contents Executive summary Data quality control Dataset generation and infilling procedure Selection of regionally-representative sites Wind Time series extension Wind gust analyses Limitations and caveats References Appendix A Stations selected Appendix B Regression analyses wind Appendix C Daily maximum wind gust record availability Tables Table 1: Descriptive statistics for maximum wind gusts at each of the 30 New Zealand locations. 8 Figures Figure 1: Annual count of days where maximum wind gust > 99th-percentile (104.8 km/hr) for Invercargill. 9 Figure 2: Annual maximum wind gust speed for Christchurch. 10 Figure 3: Annual mean daily maximum wind gust speed for Whangarei. 10 Damaging Wind Gust Index

4 Executive summary The Ministry for the Environment (the Ministry) acquired climate data from 30 climate stations from NIWA for the 2015 Environmental Reporting Synthesis Report (Environment Aotearoa 2015). These data were extracted from NIWA s National Climate Database. NIWA subsequently updated these datasets to extend the length of some data time series and infill missing data. The Ministry requested NIWA to calculate a damaging wind gust index which will contribute to the 2017 Atmosphere and Climate environmental report, using these extended, infilled datasets. The index selected was days where the maximum wind gust was greater than the 99th-percentile wind gust speed. In addition, the Ministry requested the calculation of monthly and annual highest daily maximum wind gust, and monthly and annual mean daily maximum wind gust data. These indices were initially calculated for Auckland as a proof of concept for the work (Macara, 2017a). This report details the methodology used to calculate the wind gust indices, and provides examples of the resulting data generated for the New Zealand locations. Wind gust indices were successfully calculated for all 30 locations, and the datasets are provided to the Ministry along with this report. Some gaps in the wind data series at some locations remain, largely as a result of the lack of station data available at these locations. 4 Damaging Wind Gust Index

5 1 Data quality control All wind data prepared for the Ministry and described in this report were derived from raw data values extracted from NIWA s National Climate Database (CLIDB). These raw data can be accessed for free from and while no guarantee is made regarding the accuracy of the data, all reasonable skill and care has been applied so that the data in the database are as reliable as possible. The following quality control procedure is ongoing, and undertaken for all data in CLIDB. As observed values are transferred into permanent data tables in the database (e.g. MAX_MIN_TEMP, RAIN etc.) from temporary input tables (e.g. RMS_AWS, RMS_DLYCLI etc.) they are automatically inspected for errors. These are either gross errors when values fall outside very wide universal limits so that an error flag is given the value E and the observation is not transferred into CLIDB, or they are potential errors as they lie sufficiently outside of the 1 or 99 percentile for that place/time so that an error flag is given the value W and the observation is transferred into CLIDB. Most data originating from the various data streams entering CLIDB (i.e. of different origins or message types) do so as frequently as possible (e.g. RMS_AWS is hourly but the suite of UPPER_AIR messages are every 6 hours). These frequent transfers do not report errors and warnings but daily collectives are also run and the errors and warnings are reported with these runs. The daily collective runs also log any errors or warnings into the ERRLOG and WARNLOG tables except for AWS data which do not have a daily collective but a daily reporting/logging process runs just after midnight. Time series plots centred on the observation with a W warning are generated and the 1 and 99 percentiles are also used to standardise observations and facilitate manual checking of the data. After manual inspection and depending upon the inspection outcome, the data are either unchanged or corrected (with associated quality flags) or deleted from CLIDB. Data remaining in the database are deemed sufficiently high quality for inclusion in the station data record, and for subsequent data analysis. Should users of the climate data query its validity, then additional user-initiated manual data checks are also made. 2 Dataset generation and infilling procedure Original wind datasets for 30 New Zealand locations were obtained from CLIDB, and updated by infilling missing data. This was achieved by using daily station data from nearby locations. In addition, time series extensions were performed where older (mostly closed) climate stations were located very near to the currently open station locations. Section 2.2 describes the methods employed to infill missing data for wind. Section 2.3 outlines the methods used for time series extension. A list of climate stations used for time series extension and infilling missing data at each location is provided in Appendix A. Regression equations and associated R 2 values for the analyses pertaining to daily maximum wind gusts are provided in Appendix B. Appendix C lists the period of daily wind records available at each site as a result of the procedures described in Sections 2.1, 2.2 and 2.3. Damaging Wind Gust Index 5

6 2.1 Selection of regionally-representative sites As described in Tait et al. (2014), the following criteria were used to select regionally-representative climate stations: 1. The station must currently be open (as at December 2016); 2. The station is likely to remain open for the foreseeable future; 3. The station has a long record of reliable good-quality data; 4. The station is located near a large city (e.g. at an airport site) so is representative of the climate where many people in the region live; One station per region is to be selected. However, if deemed necessary, two or three stations may be selected to represent a large region. 2.2 Wind Virtual Climate Station Network (VCSN) data are not appropriate for infilling purposes as a) there is no wind gust variable in in the VCSN dataset, and b) daily wind speed data in the VCSN dataset only start in Therefore, data from nearby climate stations was used instead. For each of the 30 New Zealand locations selected, the following infilling process was used: 1. Select a climate station ( primary station ) and obtain daily maximum wind gusts for the period 1 January 1972 to 31 December Identify missing daily values; 2. Extract daily maximum wind gust data from nearby climate stations for the period 1 January 1972 to 31 December 2016; 3. Produce regression plots between the primary station data and data from each nearby station, then estimate the wind gust values at the primary site based on the nearby station data, according to the regression equation. 4. Substitute missing primary station data with adjusted nearby station data. Nearby station data was only substituted for the period of time that the primary station was operating. Substitute data from nearby stations was only used if the R 2 from the associated regression equation was > Where more than one nearby station was used to substitute data, the nearby station with the highest R 2 was predominantly used to infill missing primary station data. 5. Recalculate monthly and annual statistics (i.e. monthly and annual days of winds gusts exceeding 99th-percentile (p99), highest daily maximum wind gust and mean daily maximum wind gust) using original and infilled daily maximum wind gust data. No minimum number of days of primary station data per month was required for a monthly value to be calculated. A monthly/annual value was calculated if there were no missing days of data for that month/year. Note that the p99 maximum wind gust was calculated from all available daily maximum wind gust data. 6 Damaging Wind Gust Index

7 2.3 Time series extension In some cases older (mostly closed) climate stations were located very near to the currently open station locations (i.e. often located within 500m of each other). Where possible, the data records from these older stations were used to extend the data series back in time. The following steps were used to extend time series for wind: 1. Obtain all available daily climate data for both the open and closed climate stations; 2. Produce regression plots using the overlapping data of the open and closed climate stations, then estimate the open station values based on the closed station data, according to the regression equation; 3. Merge the two time series to create an extended time series, using open station data where available; 4. Recalculate monthly and annual statistics. In some cases, there was no overlap between the open and closed station data. In this case, daily maximum wind gust data were merged without any adjustment to the closed station data. As such, there is no measure of comparison between the open and closed station data in these merged datasets. Note that the wind gust data at Auckland Airport was identified as dubious prior to As such, an agreement was reached with the Ministry whereby wind data for Auckland would only be provided from 1995 onwards (Macara & Tait, 2015). 3 Wind gust analyses This section shows descriptive statistics for maximum wind gusts at each of the 30 New Zealand locations selected (Table 1). Wind gust indices were originally generated for Auckland as part of a proof of concept report provided to the Ministry (Macara, 2017a). Example plots of the indices calculated for the 30 stations from the daily maximum wind gust datasets are shown in Figures 1, 2 and 3. On average, the p99 daily maximum wind gust will be exceeded on approximately 3.6 days per year. Therefore, annual counts higher than this indicate more days than usual with very strong wind gusts recorded, whereas annual counts lower than 3.6 indicate fewer strong wind gust days than usual. Christchurch and Timaru each recorded their highest wind gust on record on 1 August The winds were associated with a deep depression (low pressure system) to the south of New Zealand and an approaching cold front, which generated extremely strong northwesterly winds over the South Island. Winds were strongest in eastern parts of Canterbury where wind gusts exceeded 160 km/hr. The winds caused significant and widespread damage, with 12,600 insurance claims for structural damage, and a total insurance industry payout of $58.5 million 2010 dollars ( Damaging Wind Gust Index 7

8 Table 1: Descriptive statistics for maximum wind gusts at each of the 30 New Zealand locations. Location Year records began Highest maximum wind gust (km/hr) Highest annual total days p99 exceeded Auckland (2010) 8 (2002, 2014) Blenheim (1975) 14 (1972, 1975) Christchurch (1975) 15 (1972) Dannevirke (2007) 6 (2011) Dunedin (1984) 13 (1995) Gisborne (1994) 10 (1996) Gore (1997) 10 (1998) Hamilton (2010) 13 (1980) Hokitika (1979) 9 (1975) Invercargill (1997) 12 (1988) Kerikeri (2014) 11 (2011) Lake Tekapo (2015) 12 (2006) Masterton (2012) - Milford Sound (1994) 7 (1994) Napier (1977) 12 (1988) Nelson (1975) 10 (1987) New Plymouth (1982) 10 (2016) Queenstown (1981) 11 (2014) Reefton (2016) 7 (2002, 2004, 2010) Rotorua (2010) 8 (1988, 2012) Tara Hills (2003) 10 (1997) Taumarunui (2012) 4 (2013) Taupo (1982, 1985) 10 (1985, 2004) Tauranga (1977) 11 (1975) Timaru (1975) 18 (1988) Waiouru (2016) - 8 Damaging Wind Gust Index

9 Gust days > 99p Location Year records began Highest maximum wind gust (km/hr) Highest annual total days p99 exceeded Wellington (1973) 11 (1977) Whanganui (2004) 10 (2004) Whangaparaoa (2014) 9 (2014) Whangarei (1988) 15 (1976) Invercargill Year Figure 1: Invercargill. Annual count of days where maximum wind gust > 99th-percentile (104.8 km/hr) for Damaging Wind Gust Index 9

10 Mean daily maximum wind gust (km/hr) Maximum wind gust (km/hr) Christchurch Year Figure 2: Annual maximum wind gust speed for Christchurch. Whangarei Year Figure 3: Annual mean daily maximum wind gust speed for Whangarei. The overall average daily maximum wind gust for the period was 38.7 km/hr. Note that values of -5 indicates years where there were missing days of data, therefore an annual value wasn t calculated. 4 Limitations and caveats The data infilling efforts outlined in this report have resulted in a considerable reduction in the gaps of wind count statistics overall. However, some gaps in the wind data series at some locations remain. This is largely as a result of the lack of station data available at a given location. Specifically, if a station wasn t operating (i.e. no climate data available), then no attempt was made to infill the missing data. This is based on a prior agreement made with the Ministry, whereby data would only be supplemented by nearby station data if at least some original station data was available for the month in question. 10 Damaging Wind Gust Index

11 Infilling of wind gust data for a given location could not be done using VCSN data, and was hampered by missing daily maximum gust data at the respective nearby stations. Furthermore, the regression results showed that these data were less comparable than for rainfall (Macara, 2017b). This suggests that the infilled wind gust data at a given location should be treated with less confidence than the infilled rainfall data. Observed wind speeds demonstrate high spatial variability due to modification by local topography. Therefore, wind speeds can differ considerably between different sites at a given town or city. In some cases, no suitable station data was available to infill missing wind gust data. This was not an issue for the rainfall data, due to the consistent spatial coverage of VCSN gridpoints. Climate data for each station used in these analyses were not homogenised. Inhomogeneities in climatic time series can be caused by changes in station location, station exposure, instrumentation and observing practices. An assessment of the temporal homogeneity of data used could provide added confidence to the results obtained from these analyses. Note that the p99 wind gust speed may be below what might be considered to be damaging at a given location, however this threshold enables the capture of a reasonable number of relatively strong potentially damaging wind gust occurrences. The use of a percentile threshold means that the index is relative to each location, and is better suited to capturing relatively strong potentially damaging wind gust occurrences compared to the use of an absolute value (e.g. 100 km/hr). This is because some locations are typically subject to stronger winds than others. For example, Wellington will record maximum wind gusts in excess of 100 km/hr far more frequently than Nelson, so it is likely that Wellington would observe higher wind speeds before vegetation was damaged compared to Nelson. Essentially, areas exposed to stronger winds become wind-hardened and vegetation may have a higher tolerance to strong wind gusts. 5 References Macara, G. (2017a) Damaging Wind Gust Index: Proof of concept report. Client report no WN. Prepared for the Ministry for the Environment, 15p. Macara, G. (2017b) New Zealand Rainfall Intensity Indices. Client Report no WN. Prepared for the Ministry for the Environment, 21p. Macara, G. and A. Tait (2015) Infilling of missing climate data for the 2015 Environmental Synthesis Report: Temperature, Rainfall and Wind. Client report no. WLG Prepared for the Ministry for the Environment, 37p. Tait, A.; G. Macara and V. Paul (2014) Preparation of climate datasets for the 2015 Environmental Synthesis Report: Temperature, Rainfall, Wind, Sunshine and Soil Moisture. Client Report no. WLG Prepared for the Ministry for the Environment, 27p. Damaging Wind Gust Index 11

12 Appendix A Stations selected Note: If more than one primary station is listed for a given location, then these data were merged (i.e. the data record was extended back in time) according to the methods outlined in Section 2. Location Primary station (agent #) Stations used for infill (agent #) Auckland Auckland Aero (1962) Pukekohe Ews (2006) Blenheim Blenheim Aero (4326) Blenheim Aero (4322) Awatere Valley, Dashwood Raws (18468). Vernon Lagoon (4411). Christchurch Christchurch Aero (4843) Christchurch, Kyle St Ews (24120). Winchmore Ews (4764). Timaru Aero (5086). Dannevirke Dannevirke Ews (26958) Waione Raws (12636). Dunedin Dunedin, Musselburgh Ews N/A. (15752) Dunedin, Musselburgh (5402) Gisborne Gisborne (2810) Gisborne Aero (2807) Gisborne Ews (24976). Napier Aero (2980). Napier Aero (2977). Gore Gore (5778) Invercargill Aero (11104). Invercargill Aero (5814). Hamilton Hamilton (2112) Hamilton Aero (2110) Hamilton, Ruakura Ews (12616). Hamilton, Ruakura 2 Ews (26117). Pukekohe Ews (2006). Tauranga Aero (1612). Westport Aero (7342). Greymouth Aero Ews (23934). Tiwai Point Ews (5823). Hokitika Hokitika (3910) Hokitika Aero (3909) Invercargill Invercargill Aero (11104) Invercargill Aero (5814) Kerikeri Kerikeri Aerodrome Purerua (1196). Kaikohe (1134). (37258) Lake Tekapo Lake Tekapo Ews (24945) Tara Hills (5212). Masterton Masterton, Te Ore Ore Cws N/A. (37662) Milford Sound Milford Sound (18309) N/A. Milford Sound (4107) Napier Napier Aero (2980) Napier Aero (2977) Whakatu Ews (15876). Gisborne (2810). Gisborne Aero (2807). Nelson Nelson (4271) N/A. Nelson Aero (4241) New Plymouth New Plymouth (2283) New Plymouth Aero (2282) Cape Egmont (3497). Hawera (25222). Stratford Ews (23872). Queenstown Queenstown Aero Cromwell Ews (26381). (5451) Queenstown Aero (5450) Reefton Reefton Ews (3925) N/A. Rotorua Rotorua Aero (1770) Rotorua Aero 2 (1768) Whakatane Aero (1673). Whakatane Aero (1672). 12 Damaging Wind Gust Index

13 Tara Hills Tara Hills (5212) Pukaki Aerodrome (36596). Lake Tekapo Ews (24945). Mt Cook Ews (18125). Taumarunui Taumarunui (35135) N/A. Taupo Taupo (1858) Taupo Aero (1856) Rotorua Aero (1770). Rotorua Aero 2 (1768). Turangi 2 Ews (25643). Tauranga Tauranga Aero (1615) Rotorua Aero (1770). Rotorua Aero 2 (1768). Tauranga Aero (1612) Timaru Timaru Aero (5086) Timaru Aero (5084) Pukekohe Ews (2006). Oamaru (25937). Ashburton Aero (26170). Orari Estate Cws (35704). Christchurch Aero (4843). Waiouru Waiouru Airstrip N/A. (39148) Wellington Wellington, Kelburn Wellington Aero (3445) (25354) Wellington, Kelburn (3385) Whanganui Wanganui, Spriggens Park Ews (3715) Wanganui (3719). Palmerston North (3243). Whangaparaoa Whangaparaoa (1400) Auckland, North Shore Albany Ews (37852). Whangarei Whangarei Aero (1287) Whangarei Aero (1283) Warkworth (1374). Leigh 2 Ews (1340). Damaging Wind Gust Index 13

14 Appendix B Regression analyses wind The following table shows the regression equation and associated R 2 value for analyses pertaining to daily maximum wind gusts. Original Comparison Regression R 2 Auckland Aero Pukekohe Ews x Blenheim Aero Awatere Valley, x Dashwood Raws Blenheim Aero Vernon Lagoon x Christchurch Aero Christchurch, Kyle x St Ews Christchurch Aero Winchmore Ews x Christchurch Aero Timaru Aero x Dannevirke Ews Waione Raws x Dunedin, N/A Musselburgh Ews Gisborne Gisborne Aero x Gisborne merged Gisborne Ews x Gisborne merged Napier merged x Gore Invercargill Aero x Gore Invercargill Aero x Hamilton merged Hamilton, Ruakura x merged Hamilton merged Pukekohe Ews x Hamilton merged Tauranga Aero x Hokitika Hokitika Aero x Hokitika merged Westport Aero x Hokitika merged Greymouth Aero x Ews Invercargill Aero Invercargill Aero x Invercargill merged Tiwai Point Ews x Kerikeri Aerodrome Purerua x Kerikeri Aerodrome Kaikohe x Lake Tekapo Ews Tara Hills x Masterton, Te Ore N/A Ore Cws Milford Sound N/A merged Napier merged Whakatu Ews x Napier merged Gisborne merged x Nelson Nelson Aero x New Plymouth New Plymouth Aero x Damaging Wind Gust Index

15 New Plymouth Cape Egmont x merged New Plymouth Hawera x merged New Plymouth Stratford Ews x merged Queenstown Aero Queenstown Aero x Queenstown Cromwell Ews x merged Reefton Ews N/A Rotorua merged Whakatane x merged Tara Hills Pukaki Aerodrome x Tara Hills Lake Tekapo Ews x Tara Hills Mt Cook Ews x Taumarunui N/A Taupo merged Rotorua merged x Taupo merged Turangi 2 Ews x Tauranga merged Rotorua merged x Tauranga merged Pukekohe Ews x Timaru merged Oamaru x Timaru merged Ashburton Aero x Timaru merged Orari Estate Cws x Timaru merged Christchurch Aero x Waiouru Airstrip N/A Wellington, Kelburn Wellington, x Kelburn Wellington merged Wellington Aero x Whanganui, Whanganui x Spriggens Park Ews Whanganui, Palmerston North x Spriggens Park Ews Whangaparaoa Auckland, North x Shore Albany Ews Whangarei merged Warkworth x Whangarei merged Leigh 2 Ews x Damaging Wind Gust Index 15

16 Appendix C Daily maximum wind gust record availability The following table shows the period of time for which daily maximum wind gust data were available at each station. If the station is still operating (at the time this report was written), then the end date is present. Station (agent #) Daily maximum wind gust data availability Ashburton Aero (26170) 11/2/2006 present Auckland Aero (1962) 1/7/1971 present Auckland, North Shore Albany Ews (37852) 23/12/2009 present Awatere Valley, Dashwood Raws (18468) 10/8/ /9/2013 Blenheim Aero (4322) 1/1/ /3/1987 Blenheim Aero (4326) 15/9/1990 present Cape Egmont (3497) 1/1/1972 1/8/1985 Christchurch Aero (4843) 1/1/1972 present Christchurch, Kyle St Ews (24120) 1/11/2002 present Cromwell Ews (26381) 6/4/2006 present Dannevirke Ews (26958) 14/6/2007 present Dunedin, Musselburgh (5402) 1/7/ /8/1997 Dunedin, Musselburgh Ews (15752) 8/8/1997 present Gisborne Aero (2807) 1/1/ /12/1991 Gisborne (2810) 1/6/1990 present Gisborne Ews (24976) 30/11/2012 present Gore (5778) 13/7/1986 present Greymouth Aero Ews (23934) 29/1/ present Hamilton Aero (2110) 1/7/ /9/1988 Hamilton (2112) 1/6/1990 present Hamilton, Ruakura 2 Ews (26117) 10/11/2005 present Hamilton, Ruakura Ews (12616) 25/10/ /2/2007 Hawera (25222) 29/1/2004 present Hokitika Aero (3909) 1/1/ /3/2006 Hokitika (3910) 17/10/1991 present Invercargill Aero (5814) 1/1/ /11/2009 Invercargill Aero (11104) 2/2/1995 present Kaikohe (1134) 15/11/1985 present Kerikeri Aerodrome (37258) 1/7/2009 present Lake Tekapo Ews (24945) 19/6/ /5/2015 Leigh 2 Ews (1340) 1/1/1973 present Masterton, Te Ore Ore Cws (37662) 23/9/2009 present Milford Sound (4107) 1/10/ /3/1996 Milford Sound (18309) 1/5/2007 present Mt Cook Ews (18125) 30/3/2000 present Napier Aero (2977) 1/11/ /12/1989 Napier Aero (2980) 3/10/1990 present Nelson Aero (4241) 1/1/ /12/1991 Nelson (4271) 30/11/1991 present New Plymouth Aero (2282) 1/1/ /12/1991 New Plymouth (2283) 2/11/1991 present Oamaru (25937) 22/9/2005 present 16 Damaging Wind Gust Index

17 Orari Estate Cws (35704) 10/7/2008 present Palmerston North (3243) 1/9/1991 present Pukaki Aerodrome (36596) 23/12/ present Pukekohe Ews (2006) 21/2/ present Purerua (1196) 9/11/2013 present Queenstown Aero (5450) 1/1/ /9/1992 Queenstown Aero (5451) 29/10/1991 present Reefton Ews (3925) 21/4/1999 present Rotorua Aero 2 (1768) 1/1/ /12/1991 Rotorua Aero (1770) 16/11/1991 present Stratford Ews (23872) 13/6/2002 present Tara Hills (5212) 21/4/1985 present Taumarunui (35135) 31/1/2008 present Taupo (1858) 1/6/1990 present Taupo Aero (1856) 21/12/ /11/1989 Tauranga Aero (1612) 1/3/ /2/1989 Tauranga Aero (1615) 1/6/1990 present Timaru Aero (5084) 1/1/ /11/1989 Timaru Aero (5086) 4/6/1990 present Tiwai Point Ews (5823) 2/2/1971 present Turangi 2 Ews (25643) 6/3/2003 present Vernon Lagoon (4411) 1/2/ /6/1984 Waione Raws (12636) 5/9/1996 present Waiouru Airstrip (39148) 2/7/2011 present Wanganui (3719) 14/12/2012 present Wanganui, Spriggens Park Ews (3715) 29/2/1996 present Warkworth (1374) 1/4/ /9/1999 Wellington Aero (3445) 1/1/1972 present Wellington, Kelburn (3385) 1/1/ /12/2008 Wellington, Kelburn (25354) 13/7/2004 present Westport Aero (7342) 13/10/1991 present Whakatane Aero (1672) 1/12/ /11/1988 Whakatane Aero (1673) 6/8/1990 present Whakatu Ews (15876) 12/9/ present Whangaparaoa (1400) 29/2/2012 present Whangarei Aero (1283) 1/3/ /3/1988 Whangarei Aero (1287) 10/8/1990 present Winchmore Ews (4764) 1/8/ present Damaging Wind Gust Index 17