On the use of data assimilation in the assessment of the cost/benefit of meterological observations and observing systems. Lars Peter Riishojgaard Director, Joint Center for Satellite Data Assimilation Chair, OPAG-IOS, WMO Commission for Basic Systems 6 th WMO Data Assimilation Symposium
Overview • Money and weather forecasting – Enabling capabilities • Data and weather forecasting – Data requirements – Cost of meteorological observations • Data impacts and the WMO RRR • Can we put a monetary value on individual observing systems and/or individual observations? (and should we?) – Role of Data Assimilation community and WMO RRR • Final remarks WMO Data Assimilation Symposium, 2 College Park Oct 7-11 2013
(discussed during CBS TECO in Windhoek, 2010) Weather Prediction and the US Economy; A Macroscopic View • Department of Commerce: “ 20% of overall US economy is weather sensitive ” : ~$3 trillion/year – Impact to air and surface transportation, agriculture, construction, energy production and distribution, etc. • Assume that half of this is “ forecast sensitive ” : $1.5 trillion/year • Assume that the potential savings due to weather forecasting amount to 5% of the “ forecast sensitive total ” : ~$75B/year 3 WMO Data Assimilation Symposium, College Park Oct 7-11 2013
(discussed during TECO in Windhoek, 2010) … a Macroscopic View … (II) • “ Perfect forecast ” is an NWP run with useful skill at two weeks! • 0 h useful forecast range => $0 in savings • 336 h useful forecast (two weeks maximum predictability) range => $75B in savings • Assume now that the savings are distributed linearly over the achieved forecast range for the global NWP system: – $75B/336h ~ $223B/hr • This implies that the value to the United States economy of weather observations, dissemination, forecast products and services is >$220M per hour of forecast range per year ! 4 WMO Data Assimilation Symposium, College Park Oct 7-11 2013
The global picture • The amount of $75B/year is one estimate of the magnitude of the total potential socioeconomic benefit of weather prediction activities to the US economy • Scaling exercise, using World Bank (2011) numbers : • Annual GDP of United States: ~$15T • Annual GDP of all nations combined: ~$70T – Assuming on average (i) equal sensitivity to weather, and (ii) equal potential benefits from ability to predict across all nations, we get an estimated $75B *($15T/$70T) = $350B as the total global potential benefit of weather prediction activities (indicating a likely range of $100B to $1T) WMO Data Assimilation Symposium, 5 College Park Oct 7-11 2013
Weather Prediction Enabling Capabilities 1. Observing Systems (GOS0 2. Dissemination Systems 3. Numerical Weather Prediction – Science (modeling, data assimilation) – High-end computing • 1, 2 and 3 are of a foundational nature • Among the foundational capabilities, 1 represents the single largest expenditure WMO Data Assimilation Symposium, 6 College Park Oct 7-11 2013
NWP requirements for upper- air data coverage Hence the need for a global observing system, irrespective of target location of forecast! 7 WMO Data Assimilation Symposium, College Park Oct 7-11 2013
Estimating the total cost of running all components of the GOS • Informal exercise launched by the WMO Commission for Basic Systems in 2012 • Approach was (perhaps too?) simple: – Survey a small, but representative number of WMO member states about their total annual investment in running, maintaining and updating the GOS – Use Cost-to-GDP ratios to extrapolate that to the rest of the WMO members – Add estimates for external (non-NMHS owned) contributions: • Satellite systems • Aircraft observations • Third party networks • Marine observations • … WMO Data Assimilation Symposium, 8 College Park Oct 7-11 2013
NMHS input to survey (anonymous) Country GOS cost Expenditure Comment (K USD, 2011) on GOS, GDP fraction 1 8330 1.3 x 10 -5 Excludes radar data 2 39096 1.1 x 10 -5 3 7793 1.6 x 10 -3 (LDC) 4 14400 2.3 x 10 -4 (Amortization unclear) … … … … WMO Data Assimilation Symposium, 9 College Park Oct 7-11 2013
What do we think we know about the global cost of acquiring the observations? • Ratios are too disparate to be used for scaling, but the running costs of the conventional parts of the GOS appear to be in the single-digit $B-range • “ Non-NMHS ” -provided data (satellite, third-party networks, marine, aircraft observations) add an estimated $5B total • Total costs of all meteorological observations add up to an estimated $5-10B per year • That is a lot of money, and decision-makers are on the hunt for justification and objective metrics ! WMO Data Assimilation Symposium, 10 College Park Oct 7-11 2013
What do NWP and data assimilation have to do with this? Objective, quantitative metrics: NWP poses a well-defined prediction problem with a “ right ” answer (and an infinity of wrong ones) Well-defined measures for quality of output Well-established methodologies for assigning merit (or blame) to individual observing systems WMO Data Assimilation Symposium, College Park Oct 7- 11 11 2013
WMO Commission for Basic System, Rolling Review of Requirements Under its Commission for Basic Systems WMO maintains a standing Expert Team (ET-EGOS) which is responsible for documenting Observational data requirements for all 12 WMO 1. application areas (database) Capability of all relevant observing system (databases) 2. Statements of Guidance, or gap analyses, matching 1. 3. against 2 (a brief narrative for each application area) … 4. ET-EGOS is helped in its work by numerous other ET ’ s and Rapporteurs and by the WMO NWP Impact Workshops taking place once every four years WMO Data Assimilation Symposium, College Park Oct 7- 12 11 2013
WMO Workshops on the Impact of Various Observing Systems on NWP Five Workshops so far (the last two in close collaboration with THORPEX): • 1 st - Geneva, 1997 • 2 nd – Toulouse, 2000 • 3 rd – Alpbach, 2004 • 4 th – Geneva, 2008 - Workshop Report available on http://www.wmo.int/pages/prog/www/OSY/Reports/NWP-4_Geneva2008_index.html • 5 th – Sedona AZ, May 2012 http://www.wmo.int/pages/prog/www/OSY/Meetings/NWP5_Sedona2012/Final_Report.pdf Workshops aim to bring together scientists from all major NWP centers to assess the contribution to forecast skill of various elements of the global observing system; guidance to participants provided well in advance of Workshop itself. WMO Data Assimilation Symposium, 13 College Park Oct 7-11 2013
OSE and FSO (I) Jung et al., WMO Impact Workshop in Sedona, May 2012 • OSEs (Observing System Experiments) are based on data denial (or addition) • Impact focuses on the medium to long range • Results show the impact of withdrawing (or adding) certain data • OSE results are absolute; e.g. “ observing system X extends the useful forecast range by N hours in the NH ” WMO Data Assimilation Symposium, College 14 Park Oct 7-11 2013
OSE and FSO (II) • FSO (Forecast Sensitivity to Observations) Gelaro et al, Fifth WMO Impact Workshop, Sedona 2012 are based on the adjoint of the model/analysis system or an ensemble approach • Approach focuses exclusively on the short (quasi-linear) range • Results show the impact of observations in the presence of all other observations • FSO measures of impact are relative (e.g. often expressed in percentages that add up 100, even for poor forecasts or poor system performance) WMO Data Assimilation Symposium, College 15 Park Oct 7-11 2013
4 th WMO Impact Workshop, Alpbach 2008; impact summary slide Overall impact ( “ marginal skill ” ) on global NWP WMO Data Assimilation Symposium, 16 College Park Oct 7-11 2013
Some Preliminary Conclusions from the Fifth WMO Impact Workshop in Sedona, May 2012 • Modern, 4-dimensional data assimilation methods (4D-VAR, ENKF) have led to greatly improved use of data, especially of – Asynoptic data (e.g. aircraft, satellite observations) – Observations with complex relationships between measured and model variables (satellite radiances, GPSRO, radar,…) • Broad consensus about highest-ranking contributors to forecast skill, but not necessarily about their ranking order: – AMSU-A (microwave temperature sounder) – AIRS/IASI (hyper-spectral infrared sounders) – Radiosondes – Aircraft observations – Atmospheric motion vectors (feature tracking satellite winds) WMO Data Assimilation Symposium, 17 College Park Oct 7-11 2013
Some Preliminary Conclusions (II) • Radio occultation data (GPSRO) also have substantial impact but data volumes are currently declining as COSMIC is approaching the end of its lifetime • There is now no single, dominating satellite sensor; several sensors contribute to forecast skill in roughly equal measure • The relative impacts of specific observation types depends on which other observations are used and how – If certain data are withheld, other datatypes can in some contexts compensate for the lost skill • However, the continued value of in situ data, and in particular of wind measurements, was clearly demonstrated • Regional data assimilation systems making progress in the use of radar and satellite observations – Radiance assimilation still problematic WMO Data Assimilation Symposium, 18 College Park Oct 7-11 2013
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