ࡱ> qsnop qbjbj<< U"^^i-N N 4CCCChTtCZ`Rs("( _______bNeb_ _` J__D0G7&ޞCWF)F$H*`0Z`MFeeHGGN :    WORLD METEOROLOGICAL ORGANIZATION ________________________ INTERGOVERNMENTAL OCEANOGRAPHIC COMMISSION (OF UNESCO) ________________________Joint WMO-IOC Technical Commission For Oceanography And Marine Meteorology (JCOMM) Observations Programme Area Coordination Group (OCG) Fourth Session Hobart, Australia, 18-20 April 2011 OCG-IV/Doc.  FORMTEXT 2.2.3 (29.III.2011) ______ English only Services and Forecast Systems Programme Area report on observations for Marine Weather Services submitted by Ming Ji, SFSPA Coordinator Real time marine wind and wave observations are critical for operational forecasting of marine wind and waves for navigation safety and mitigation of coastal storm hazards. In addition to initialization for NWP models, operational marine forecasters rely on real time wind and wave observations to identify current atmospheric and oceanic conditions, assessing model guidance that are available to them, and verify previous forecasts. This report is to be complementary to the report on WMO RRR and the report by the ETOOFS Chair on ocean observing requirements. Wave observations 1.1 Near real time wave observations are obtained through voluntary ships, weather buoy networks, and satellite altimeters. Wave buoys can provide accurate, real time wave height, direction and frequency observations. The buoy networks are the best source of operational wave observations. However, buoy networks are mostly limited to near coastal areas. No wave buoys are available in the open ocean. Voluntary ships provide real time wave observations over the open ocean, along major shipping lanes. However, the measurement has high uncertainties, due to the dependency on observers. Also, ships generally avoid severe storm conditions in the open ocean, thus, there is no coverage over areas of severe storms. Satellite altimeters could observe near real time (NRT) wave height accurately for the open ocean. Their limitations include unable to observe wave directions and periods, and unable to cover near coastal areas. 1.2 The requirements for wave measurements are largely incorporated in the input for the RRR by ETWS (see Appendix A). This report will highlight pilot efforts in wave measurement (in situ) evaluation and test (PP-WET) and status of operational applications using the altimeter platform. The PP-WET effort is a joint DBCP/ETWS activity. The project includes a US (NDBC) plan to deploy a wave rider next to the Monterey Canyon operational buoy and the MBARI buoy, Korea contribution at the Ieodo platform, Norways effort towards evaluation at the Ekofisk platform, and the operational Canadian co-deployments. For details see the project web site  HYPERLINK "http://www.jcomm.info/WET" www.jcomm.info/WET. One significant gap in in situ wave observation is the limited spatial coverage, which is still linked to the continental margins. There is a separate ETWS-led pilot project to develop wave measurement capability from drifters (PP-WMD, led by David Meldrum) which has the potential to make a significant impact on closing the gap. Near real time wave height measurement from altimeters are now used in marine forecast operations. These include the Canadian weather service is accessing the entire GlobWave data base for validation of their wave hindcast products and identification of global extreme sea states. The U.S. weather service uses altimeter wave height data for operational sea state analyses, and verification for open ocean wave forecasts (see Appendix B). Ocean surface vector wind observations 2.1 There are limited in situ observations of marine winds from buoy networks and voluntary observing ships. The most significant gap is the lack of spatial coverage, especially over the open ocean. Satellite based scatterometer platform provides unprecedented spatial coverage of marine winds over the open ocean. The QuikSCAT has proven to be invaluable for both initialization of operational NWP models (e.g., ECMWF) and operational marine forecaster applications (e.g., NCEP). 2.2 The demise of the QuikSCAT in Nov. 2009 left a significant gap for marine forecasting operations. However, the gap is being bridged by the Europeans ASCAT and Indias OSCAT. 2.3 Ocean surface vector winds (OSVW) from Advanced SCATterometer (ASCAT) aboard EUMETSAT MetOps series of operational satellites are used operationally by a number of NMHS including the Ocean Prediction Center (OPC) of the U.S. NWS. ASCAT data are used to determine the strength and areal extent of warning category winds, to locate storm systems, and to estimate the validity of NWP analyses and short term forecasts. At this time, ASCAT is the only data source able to partially mitigate the demise of the QuikSCAT. 2.3.1 ASCAT has a low bias in high winds as compared to QuikSCAT, a lower resolution (25 km versus 12.5 km), and observes 40 percent less ocean area each day due to the dual narrow (550 km) swaths with an inherent 720 km nadir gap. NOAA NESDIS has improved the model function for wind speeds greater than 15 m/s and is now available in a parallel mode to operational forecasters. OPC uses ASCAT winds as a source to bias correct NWP 10m winds from the NCEP regional and global NWP models. OPC also usesASCAT winds to derive sea-level pressure for forecasters to reference using a planetary boundary layer model (University of Washington). 2.4 OSVW data from India's OceanSat SCATterometer (OSCAT) on the OceanSAT-2 satellite of India Space Research Organization (ISRO) are available to OPC in delayed time (approximately two days old) via ftp. OSCAT is a rotating dual pencil beam scatterometer at Ku band that yields a swath ofapproximately 1800 km. Through a multi-lateral arrangement among U.S., India, and EUMETSAT, a near real time data flow (non-operational) is being established through EUMETSAT with data delivery anticipated within 2 1/2 to 3 hours of data acquisition. 2.4.1 Preliminary evaluation suggests that OSCAT has low sensitivity to winds of 6 m/s and less, and there appears to be a high reliance on NWP background fields to determine the most likely OSVW solution. OSCAT does show potential for operational use by forecasters for higher wind applications such as determining warning category and areal extent. NOAA NESDIS is working to optimize the wind retrievals from OSCAT for operational applications. Appendix A Observation Requirements for wind waves and storm surges Val Swail, Chair, ETSS Introduction The purpose of this document is to specify the requirements for surface wave observations that arise from offshore wave forecast modelling and related activities. The document aims to present the foreseen requirement for the medium to long term (present out to c. 10 years ahead). The requirement has been prepared in consultation with the JCOMM Expert Team on Wind Waves and Storm Surges. It should be noted that this document presents the requirements for observations, without addressing the availability or otherwise of suitable technical solutions to deliver those observations. The technical solution best able to meet the requirements will depend upon the variety of observing platforms available with consideration of practicalities and costs. The observation requirements are presented as follows: firstly, a brief description of the applications for which the data are required is given, including indications of any particular issues or priorities relating to the application. Secondly, the table of requirements is specified (Table 1). Within the table the requirements for temporal and spatial sampling, accuracy and timeliness are specified. In each case two values are given: Min: the value below which the observation does not yield any significant benefit for the application in question; Max: the value above which no further significant benefits would be obtained. Spatial sampling and temporal frequency entries are based upon point data at the specified spacing, reporting with the specified frequency. Thus a requirement of 100km and 24 hours is taken to mean point observations spaced 100km apart each reporting once every 24 hours. In practice, however, it should be noted that the actual representativeness of the observations will vary between platforms, and the requirement for representativeness of the observations will depend upon model resolution. More specifically, for maximum benefit observations with a footprint smaller than a model grid box, and with temporal averaging smaller than a model timestep will deliver the maximum benefit. For applications with other quantitative requirements of particular importance, these additional requirements are noted in the table. Finally, the implications of these requirements in terms of particular observing platforms are presented (Table 2). This table is intended to provide guidance as to how best to meet the requirements given the current observing technology. Applications The applications included within the scope of this document are those related to offshore wave forecast modelling. Particular applications are: 1. Assimilation into offshore wave forecast models This application includes assimilation into both global and regional scale offshore wave models, and the requirements can be sub-divided accordingly. Assimilation is currently largely based around use of satellite observations. Altimeter wave height observations provide the most straightforward data set to use, and would generally be used alongside associated wind speed observations. SAR derived wave spectra can also be used, but present more technical challenges. In situ measurements are currently too sparse in the open ocean to be of particular value, but could potentially provide higher accuracy observations to complement (and correct for biases in) the satellite observations. In general, the availability of observations with some spatial coverage (e.g. HF radar data, swath data) would offer significant benefits for assimilation though the ability to fully initialise features on a range of scales. The requirements for observations for assimilation are dependent upon the resolution of the models employed, with a need to constrain model evolution across the model grid, and in particular a need for sufficient resolution to capture the synoptic scales. Current global model resolutions are typically of the order of 30-100km, with regional model resolutions down to 3-4km (with a natural progression to higher resolution expected). Coastal models require different observing methods to those used for the open ocean due not only to their high resolution, but also due to limitations of the satellite data close to land, hence for these models systems such as coastal HF radar are of particular importance. The real-time nature of the assimilation application together with the rapid response time of sea state parameters to changes in winds makes timeliness a priority. 2. Validation of wave forecast models The requirements for validation are driven by two main activities: real-time validation, with requirements very closely related to those for assimilation; and delayed mode validation, with requirements that place greater emphasis on accuracy, and more relaxed timeliness requirements. Requirements for the former, which is generally focussed on rapid validation of performance for particular events, are therefore included with those for data assimilation. Requirements for the latter, which is typically concerned with statistical measures of performance, are presented as a distinct category. In situ buoy data are currently the key data source for validation due to their accuracy and the availability of spectral data, particularly for delayed mode validation. Due to the dependence of the wave forecasts on surface winds, there is also significant value from use of collocated surface wind data in validation activities. However, spatial sampling of buoy data does not currently meet the requirement for validation of offshore wave models, and in consequence altimeter data are also widely used for validation of point data. As with the assimilation application, availability of observations with some spatial coverage would provide significant benefits through provision of a more spatially homogeneous validation. Again, requirements are dependent upon model resolution, though the required sampling is less dense than is required for assimilation. The key requirement, however, is to ensure that the sampling is sufficient to include a representative sample of different physical regimes globally. There is a strong requirement for improved coverage of high quality spectral observations, especially to improve representation of swell in wave forecast models. 3. Calibration / validation of satellite wave sensors Whilst the satellite instruments clearly have the potential to provide observations with synoptic global coverage, the quality and usability of these observations is dependent upon good calibration of the satellite sensors. This can only be achieved through use of a sufficiently dense network of accurate in situ measurements. Point data are required for validation of altimeter wave measurements, whilst spectral data are required for use with SAR derived wave spectra. Sampling requirements are similar to those for validation of forecast models, with the additional consideration that buoy observations located along satellite ground tracks would be of particular value. Accuracy is of greater importance than timeliness for this application. 4. Ocean wave climate and variability Determination of ocean wave climate requires a long timeseries of stable data, with sufficient sampling to capture the physical regimes of the global ocean. This application therefore involves additional requirements: stability and sustainability of the observing platform. In situ measurements provide the natural source for such a time series of data, though the open ocean in situ sampling is currently inadequate for this purpose. Satellite observations can provide complementary information, but cannot be used in isolation without the in situ observations. Timeliness is not a consideration for this application. 5. Role of waves in coupling Investigation of the role of waves in coupling requires collocated observations of a wider range of parameters than is required for the other observations, most notably air-sea flux measurements. Spatial sampling could be restricted to a small number of open ocean locations to allow processes to be studied in detail. Again, timeliness is not a consideration for this application. This application differs from the other applications in that the work in this area is generally focussed around dedicated process studies, rather than routine monitoring. Hence the requirements are more specific to particular studies, and in general are not likely to be addressed by the same platforms as the routine observation requirements. ApplicationParameterUnitAreaHorizontal Resolution (km)Temporal frequency (hours)AccuracyDelay of Availability (hours)Decadal StabilityRemarksMinMaxMinMaxMinMaxUnitsMinMax(a) Assimilation into / real-time validation of global wave forecast models(1) Significant wave heightmGlobal605240.110% 0.25m2% 0.1m% / m6No delayData with spatial coverage would be advantageous. Collocated surface wind data advantageous for real-time validation (2) Dominant wave directiondegreesGlobal605240.122.55degrees6No delay(3) Wave periodsGlobal605240.110.1s6No delay(4) 1D frequency spectral wave energy densitym2 / HzGlobal30025240.120%10%6No delay(5) 2D frequency direction spectral wave energy densitym2 / HzGlobal30025240.120%10%6No delayDirectional accuracy requirements for 2D spectra as for wave direction data.(b) Assimilation into / real-time validation of regional wave forecast models(1) Significant wave heightmRegional200.1240.110% 0.25m2% 0.1m% / m6No delayStrong requirement for data with spatial coverage. Collocated surface wind data advantageous for real-time validation.(2) Dominant wave directiondegreesRegional200.1240.122.55degrees6No delay(3) Wave periodsRegional200.1240.110.1s6No delay(4) 1D frequency spectral wave energy densitym2 / HzRegional100 1240.120%10%6No delay(5) 2D frequency direction spectral wave energy densitym2 / HzRegional100 1240.120%10%6No delay (c) Delayed mode validation of wave forecast models(1) Significant wave heightmGlobal100050240610% 0.25m1% 0.05m% / m72024 Priority is increased coverage of high quality spectral observations. Additional parameters of use for delayed mode validation (e.g. surface wind data, full time series of sea surface elevation)(2) Dominant wave directiondegreesGlobal100050240622.51degrees72024(3) Wave periodsGlobal100050240610.1s72024(4) 1D frequency spectral wave energy densitym2 / HzGlobal100050240620%10%72024(5) 2D frequency direction spectral wave energy densitym2 / HzGlobal100050240620%10%72024(d) Calibration / validation of satellite wave sensors(1) Significant wave heightmGlobal10001024110% 0.25m1% 0.05m% / m72024Collocation with satellite ground tracks advantageous (2) Dominant wave directiondegreesGlobal10001024151degrees72024(3) Wave periodsGlobal10001024110.1s72024(4) 1D frequency spectral wave energy densitym2 / HzGlobal100010024120%10%72024(5) 2D frequency direction spectral wave energy densitym2 / HzGlobal100010024120%10%72024(e) Ocean wave climate and variability(1) Significant wave heightmGlobal100010024610% 0.25m1% 0.05m% / m720240.01m Wave climate applications may rely on nearshore wave modelling. Hence validation data for use in nearshore wave models is an additional requirement for climate applications. (2) Dominant wave directiondegreesGlobal100010024622.51degrees720241.0 degrees(3) Wave periodsGlobal100010024610.1s720240.05s(4) 1D frequency spectral wave energy densitym2 / HzGlobal100010024620%10%72024(5) 2D frequency direction spectral wave energy densitym2 / HzGlobal100010024620%10%72024(f) Role of waves in coupling(1) Significant wave heightmGlobal10001060.510% 0.25m1% 0.05m% / m72024Additional collocated measurements required. Process studies likely to require dedicated dense sampling in small regions, and sampling to higher frequency than routine monitoring. Wave observations should be included routinely in studies of air-sea interaction.(2) Dominant wave directiondegreesGlobal10001060.522.51degrees72024(3) Wave periodsGlobal10001060.510.1s72024(4) 1D frequency spectral wave energy densitym2 / HzGlobal10001060.520%10%72024(5) 2D frequency direction spectral wave energy densitym2 / HzGlobal10001060.520%10%72024Table 1: Observations requirements PlatformRelevant requirementsRequired networkCommentsIn situ (non-spectral) buoysa1, c1, d1, e1, f1 a3, c3, d3, e3, f3 c4, d4, e4, f4Validation requirement is for average 10 spacing requiring a network of around 400 buoys with minimum 10% / 25cm accuracy for wave height and 1 second for wave period. Higher density would be advantageous for data assimilation. Standardised measurements and meta data are essential to ensure consistency between different stations. Provision of 1D spectra from buoys with suitable instrumentation is valuable. Primary requirement is for high quality observations for validation and calibration of altimeter data. Secondary requirement for use in assimilation. Lack of open ocean buoy observations currently makes assimilation on a global scale unfeasible. Improvement to the network would make this viable, and a potential primary requirement. An (approximately) uniform distribution of buoy observations is desirable. Collocated surface wind observations are advantageous for validation activities. Further additional parameters are of value for use in delayed mode validation. Current in situ reports are not standardised resulting in impaired utility. In situ spectral buoysc5, d5, e5, f5Validation requirement is for average 10 spacing requiring a network of around 400 buoys. Higher density would be advantageous for assimilation.Primary requirement is for high quality observations of 2D spectra for use in validation and in calibration of SAR data. At present this type of data are not widely available, and in consequence validation and calibration activities of this type are not common. Secondary requirement for use in assimilation.Satellite altimeter a1, b1, c1, e1 a3, b3, c3, e3 Minimum 20km resolution required for use in regional models. Along track spacing is likely to be adequate to meet this requirement; cross-track spacing is not. Multiple altimeters are therefore required to provide adequate cross track sampling. Fast delivery (within 6 hours at most) required with accuracy of 10% / 25cm for wave height, and 1 second for wave period. Long-term, stable time series of repeat observations required for climate applications.Primary requirements arise from data assimilation. Secondary requirement for use in validation. Swath data would be advantageous. Precise specification of wave period products is required.Synthetic aperture radar / Real aperture radara5, b5, c5100km resolution required for use in regional models, with fast delivery required (within 6 hours)Primary requirements arise from data assimilation. Secondary requirement for use in validation. Real aperture radar capability expected to be available within 5 years.Coastal radarsb1, b2, b3 c1, c2, c3High resolution observations (up to 100m resolution) required over coastal model areas. Requirement for use in assimilation into, and validation of high resolution coastal wave models.Other technologies: (e.g. Navigation radar, Other radar, Shipborne sensors such as WAVEX)allPotential contribution of these platforms should be developed where they can contribute to meeting the specified requirements.Table 2: Implications for particular observing platforms Appendix B Gridded wave forecast vs. buoy and altimeter SWH measurements The OPC wave height verification experiment used a 3-hour window for buoy and altimeter wave height data to verify wave height forecast for 0-hour (analysis), 24-hour and 48-hour forecasts. Altimeter wave height data was not used in the real time operational sea state analysis. Gridded forecast data sets were interpolated on the altimeter and buoy data points and the differences were calculated. Verification period: 03/01/2010 --- 12/14/2010 Along the track Altimeter data resolution: 0.5 Deg. (10 averaging along the track) Total data pairs (co-located forecast, obs) for 48-hr forecast: 200040 Total data pairs (co-located forecast, obs) for 48-hr forecast: 200040 Categorical verification by defining seven (7) categories (in feet): 0-3; 4-5; 6-8; 9-12; 13-16; 17-20; >20 Compute the following in each category: The overall % Hit, % Correct, % Error > 1 category. Also, Heidke, Peirce and Gerrity Skill Scores. Summary: The Contingency Table for 24-hr (Table 1) and 48-hr (Table 2) forecast shown below shows categorical hits, and misses. For24 and 48-hr forecasts, the fractions of hit (correct forecast within the same category) are 70.1% and 66.9%, respectively. The fraction of forecasts with forecast error of 2 categories or higher for 24 and 48 hour forecasts are 0.7% and 1.2%, respectively. The U.S. NWS uses a metric for wave height forecast, known as percent correct. It defines a hit as forecast wave height to be within 1 ft of verifying buoy observations. With this definition, the % correct for the same testing period, for the 48-hr forecast is 63.79%. However, such measure tends to penalize severe wave situations. We use an alternative measure which defines a hit as forecast wave height above 10 ft to be within 10% of the verifying observation. With the alternative definition, the 24-hr wave height forecast score for the testing period was 69% whereas the 48-hr forecast for the period was 64.5%. As a comparison, the percent correct for near real time sea state analyses was 72.4% which in a way, suggest a upper limit of wave height forecast skill for open ocean areas. Near real time altimeter SWH data is highly valuable for open ocean wave forecast verification. Verification enables forecasters to assess meteorological and oceanographic situations, models bias, and forecast techniques, thus improve situational awareness for subsequent forecasts. Thus NRT altimeter SWH data fill an important void of open ocean wave height observation. Note: Current data latency for altimeter wave height is about 2-3 hours before they are available for operational use by forecasters. NCEP operational wave model does not assimilate wave height data at present. However, an ECMWF report indicated that forecast from operational coupled wave-atmospheric NWP model with assimilating wave height data positively impacted atmospheric forecast out to about 9 hours. Altimeter SWH data was largely not used in the experiment period. OPC has developed a procedure to incorporate altimeter SWH data into real time operational sea state analysis using a combined automated procedure (OI) and forecaster applications of real time SWH data from buoys, ships and altimeters. This procedure becomes operational in 2011. However, due to the 2-3 hour data latency, a significant portion of the NRT altimeter SWH still does not get into the real time sea state analysis product. 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