Preliminary summary of the 2015 NEWS- e realtime forecast experiment

Transcription

1 Preliminary summary of the 2015 NEWS- e realtime forecast experiment December 15, Introduction The NOAA Warn- on- Forecast (WoF) research project is tasked with developing a regional 1- km storm- scale prediction system that can assimilate radar, satellite, and surface data. A future WoF system will likely generate new 0-3 h probabilistic forecasts 3-4 times an hour, for the purpose of predicting hazardous weather phenomenon, such as thunderstorm rotation, hail, high winds, and flash flooding. Preliminary work has begun on coarser grids using a Weather Research and Forecasting (WRF) model- based ensemble data assimilation system, known as the NSSL Experimental Warn- on- Forecast System for ensembles (NEWS- e). The NEWS- e was run in real- time each day from 4 May 5 June 2015, which coincided with the NOAA Hazardous Weather Testbed 2015 Spring Forecast Experiment. Storm- scale data assimilation experiments were generally conducted when the forecast risk from the Storm Prediction Center (SPC) of severe storms was enhanced or greater. The resultant ensemble analyses and forecasts of severe weather events from spring 2015 were produced on a 3- km event- dependent grid (intended to capture the region of primary severe weather development on that day). This 36- member storm- scale ensemble was nested within a 15- km continental United States (CONUS) ensemble constructed from initial and boundary conditions provided by members of the Global Ensemble Forecast System (GEFS). Around the time of convection initiation, radar and satellite (cloud water path retrievals) data were assimilated every 15 min using the ensemble Kalman filter (EnKF) approach encoded in the Data Assimilation Research Testbed (DART). A 90- min ensemble forecast was initialized from the resultant storm- scale analyses at the top and bottom of each hour of the storm event. As stated, a primary goal of the NEWS- e realtime forecast experiment was to test the feasibility of generating probabilistic forecasts of severe weather that are valid over shorter time windows (~3 h or less). The work toward this overarching goal allowed for an informal evaluation of the realtime system configuration, which was largely constrained by the spectrum of data latencies (ranging from ~2-15 min for the various storm- scale data sets) and may not reflect choices available to retrospective case studies. Furthermore, this work also allowed for the demonstration of new data sources for assimilation, namely the ingestion of the NSSL National 3D Reflectivity Mosaic instead of the more standard Level II reflectivity product. Finally, this work began to explore the problem of efficient data flow between research and operations, primarily through the push of WRF- based output to the Probabilistic Hazards Information (PHI) tool, which is being developed as a web- based conduit for describing the probability for a given hazardous weather phenomenon within a defined spatial extent and temporal period. 2. Methodology 1

2 The realtime strategy employed during the NEWS- e forecast experiment largely followed the methodology described in section 2 of Wheatley et al. (2015). In spring 2015, similar to Jones et al. (2016), the NEWS- e system utilized combined radar and satellite (cloud water path) data assimilation to produce storm- scale analyses. More details on the meso- and storm- scale observational data and processing for the NEWS- e system also can be found below in subsections 2a- b. Further differences between the Spring 2015 NEWS- e configuration and the system configuration described in Wheatley et al. (2015) have been enumerated below: 1. Ensemble initialization: Initial conditions for the parent and nested grids are downscaled from the 21- member 1800 UTC (day before event) Global Ensemble Forecast System (GEFS) forecast cycle, and then a 6- h forecast is made from each ensemble member. Starting at 0000 UTC (day of event), mesoscale observations are then assimilated every 1 h out to the beginning of a storm- scale experiment. 2. WRF model physics options: In 2015, the 4- layer Noah land surface model was used in lieu of the RAP land surface model. Also, these high- resolution simulations use the radiation- aware Thompson microphysics scheme. 3. Radar reflectivity data assimilation: Reflectivity observations are now obtained from the NSSL National 3D Reflectivity Mosaic (and converted directly into a DART- readable format). This 3D reflectivity grid is provided to the Warn- on- Forecast project on a fixed Polar Stereographic grid with 6- km horizontal resolution, which is bounded by latitudes 25 N and 50 N in the south- north direction and by longitudes 65 W and 105 W in the east- west direction. The 3D reflectivity grid has 10 vertical levels from 1-10 km MSL, spaced every 1 km. Nonzero reflectivity values greater than 20 dbz are assimilated in each experiment. Additionally, nonzero reflectivity values less than 10 dbz are assimilated as clear- air returns, which help reduce spurious (i.e., unobserved) convection in the model. The dbz band is effectively treated as a no- data halo. The data latency for reflectivity is ~2 min. 4. Radial velocity data assimilation: Level II radial velocity observations from five (an increase from three) WSR- 88Ds closest to the studied convection are assimilated in each experiment. The data latency for radial velocity is ~5-7 min. 5. Cloud water path retrieval data assimilation: The GOES imager takes multispectral images over the continental United States (CONUS) every 5-15 min depending on the need (Menzel and Purdom 1994). Cloud properties are retrieved from 4- km GOES imager data for pixels classified as cloudy (Minnis et al. 2008b) using the multispectral retrieval algorithm developed by Minnis et al. (2011). The retrieval algorithm calculates cloud top pressure (CTP), cloud top temperature (CTT), cloud emissivity, and cloud phase for clouds having a cloud optical thickness (COT) less than ~4. Cloud- base pressure is the pressure corresponding to the altitude equal to the difference between cloud- top height and cloud thickness. Cloud phase classifies 2

3 a cloudy pixel as either liquid or ice based on the cloud temperature and cloud effective particle size information. Optically thick clouds containing both liquid and ice phase hydrometeors are generally classified as ice clouds since the current iteration of the retrieval algorithm is unable to separately classify mixed- phase clouds. Hereafter, liquid water path (LWP) refers cloud water path associated with liquid phase clouds only while ice water path (IWP) refers to the cloud water path for ice and mixed- phase clouds. The observation error is defined as a function of both LWP and IWP values with the lowest errors defined for clear- sky retrievals, and the highest for high CWP retrievals (Table 1). LWP and IWP retrievals are smoothed to a 6- km grid prior to assimilation. This smoothing removes the unwanted variations and also produces a dataset better suited to a 3- km model using the 2ΔWP guideline described by Lu and Xu (2009). For tall clouds, geo- location errors exceeding 10 km can occur; thus, a parallax correction is also applied where CWP > 0.0 kg m - 2. The geo- location of the raw satellite data and retrievals measure the physical condition of the cloud at its top and not its base, and since the satellite is not directly overhead, the relative locations at the surface and aloft are not the same. To correct for parallax, the retrieved cloud height is used to remap cloudy pixels to their zenith location above the surface (Wang and Huang 2014). As convection matures, cloud optical thickness increases to the point where the satellite retrievals become saturated, artificially limiting CWP in high- precipitation regions. This also results in a high bias in the CBP retrieval. When only CWP retrievals are assimilated, CBP values are adjusted downward when CWP > 1.0 kg m - 2 and the original CBP < 500 hpa. However, the underestimate of CWP remains, potentially weakening already mature convection in the model analysis if assimilated. Data latency for the current GOES project is minutes. Future GOES- R products should reduce latency by at least half. Table 1. Observation errors for IWP and LWP defined as a function of the retrieval value. IWP / LWP (kg m - 2 ) Error (kg m - 2 ) < > a) Observational data and processing As noted in Wheatley et al. (2015), mesoscale observations of pressure (altimeter setting), temperature, dewpoint, and horizontal wind components are primarily obtained from the NCEP Meteorological Assimilation Data Ingest System (MADIS; Miller et al. 2007). Observations platforms included are METAR and marine surface stations (p, T, Td, u, v), National Mesonet/Integrated Mesonet (p, T, Td, u, v), rawinsondes (surface altimeter and T, Td, u, v at mandatory levels), the Aircraft Communications Addressing and Reporting 3

4 System (ACARS) (T, u, v), and the NOAA GOES (Daniels et al. 2003) (u, v). The MADIS surface observations are augmented by data from the Oklahoma Mesonet (McPherson et al. 2007) when included within the nested grid. Meso- and storm- scale observation types and errors used during the 2015 NEWS- e realtime forecast experiment are listed in Table 2 (see below). On the mesoscale, these values are consistent with those specified by NCEP in the Global Forecast System (GFS) model (similar to Romine et al. 2013, 2014). For rawinsonde and ACARS temperature and horizontal wind data, the vertical error profiles vary with height as a function of pressure. In addition to these error prescriptions, further observation processing is performed to reduce observation density and enhance system stability. All observations within five grid lengths of the mesoscale grid s lateral boundaries are not assimilated. Also, errors assigned to observations within fifteen grid lengths of the mesoscale grid s lateral boundaries are increased (by as much as a magnitude of 2.5) to mitigate the impact of large analysis increments in this transition zone. Finally, ACARS observations are superobbed to horizontal and vertical resolutions of 60 km and 25 hpa, respectively. Table 2. Observation types and errors used during the 2015 NEWS- e realtime forecast experiment. Observation type Error MESOSCALE DATA ASSIMILATION METAR temperature 1.75 K METAR dewpoint Lin and Hubbard (2004) model METAR u, v wind components 1.75 m s - 1 METAR altimeter 1.00 hpa Mesonet temperature 2.50 K Mesonet dewpoint Lin and Hubbard (2004) model Mesonet u, v wind components 2.50 m s - 1 Mesonet altimeter 1.00 hpa Oklahoma Mesonet temperature 1.75 K Oklahoma Mesonet dewpoint Lin and Hubbard (2004) model Oklahoma Mesonet u, v wind components 1.75 m s - 1 Oklahoma Mesonet altimeter 1.00 hpa Radiosonde temperature K Radiosonde dewpoint Lin and Hubbard (2004) model Radiosonde u, v wind components m s - 1 Radiosonde (surface) altimeter 1.00 hpa ACARS temperature K ACARS u, v wind components 2.50 m s - 1 Satellite u, v wind components STORM- SCALE DATA ASSIMILATION Clear- air reflectivity (i.e., radar zeros ) 5 dbz Radar reflectivity (i.e., precipitation) 5 dbz Radial velocity 3 m s - 1 b) CWP forward operator 4

5 In summary, predicted LWP and IWP are calculated at each model time step using the column- integrated cloud hydrometeor mixing ratio. For each grid point and model level, the mixing ratios of each hydrometeor variable (qc, qr, qi, qs, and qg) are summed to form a total cloud water mixing ratio, qa. For mixed- phase clouds, the total cloud water mixing ratio is then integrated over the entire atmospheric column and divided by gravity to calculate the predicted IWP value. A similar value (qliq) is generated from only the liquid phase hydrometeors for comparison with LWP retrievals. For both liquid and mixed- phase clouds, the vertical extent of the cloud layer at each observation location when CWP > 0.0 kg m - 2 is defined by CTP and CBP and is passed to the forward operator and used to constrain the CWP calculated from the model. For all clouds, the vertical coordinate of the cloud layer at each observation location is defined by the average of CTP and CBP. Clear- sky (CWP=0 kg m - 2 ) retrievals are also assimilated except that the vertical coordinate of the observation remains undefined. Both cloudy and clear- sky retrievals have a horizontal localization radius of 40 km applied. A lager 6 standard deviation outlier threshold is applied when assimilation retrievals. Testing showed that using a larger outlier threshold for satellite retrievals proved more effective than using the more restricted threshold of 3 standard deviations. This real- time data observation processing system was fashioned in such a way that removing CWP observations in heavy precipitation regions was not practical, potentially leading to underestimates of CWP in these areas. To compensate for this, the CWP forward operator includes hard limits for the prior and posterior ensemble mean LWP and IWP. The limits are 4.5 kg m - 2 for IWP and 3.5 kg m - 2 for LWP, which roughly correspond to the maximum possible retrieval values. In heavy precipitation regions, the prior ensemble mean IWP or LWP and the corresponding observation will have similar values and while the observation will be assimilated, its impact will be small due to the small innovation resulting from the limits in the forward operator. 3. Preliminary findings from the 2015 NEWS- e realtime forecast experiment a) May 6, 2015 The 6 May 2015 severe weather outbreak produced 16 confirmed tornadoes in NWS Norman forecast area. The same weather pattern also produced numerous tornado reports across Kansas and south- central Nebraska. The most significant tornadoes for this event include an EF- 3 tornado that moved near the cities of Amber and Bridge Creek in Oklahoma, another EF- 3 tornado that affected parts of southeastern Oklahoma City, an EF- 2 tornado that occurred near Archer City in western north Texas, and an EF- 1 tornado that moved through the west side of Norman, OK. Central Oklahoma was additionally impacted by severe flash flooding as the slow- moving storms began to merge and grow upscale, producing as much as 4 to 7 inches of rain over the area. 5

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7 Fig min forecasts of probabilities of 0-2 km vertical vorticity greater than s - 1 (red shading; see color bar) launched at (a) 1930 UTC 6 May 2015, (b) 2100 UTC 6 May 2015, and (c) 0100 UTC 7 May The gray lines indicate the observed tornado tracks. It is noted that the forecasts launched at 2100 UTC 6 May and 0100 UTC 7 May begin approximately 30- min before the Amber- Bridge Creek and southeastern Oklahoma City tornadoes, respectively. HIGHLIGHTS: PANEL (A): The first forecast at 1930 UTC (launched after 3 assimilation cycles) shows a relatively low- probability (~0.4 or less), albeit coherent, vorticity swath that extends east- northeast toward the Oklahoma City metropolitan area. The vorticity swath is largely contained within the 2 tornado warning issued during the 90- min forecast period, although shows some southern displacement later in the forecast time. PANEL (B): Probabilities generally increase with subsequent storm- scale updates. The forecast launched at 2100 UTC (~30 min before the Amber- Bridge Creek tornado) shows probabilities of ~ collocated with several tornado warnings issued during the 90- min forecast period, as well as the assessed damage path. The vorticity swath extends northeast over parts of west Norman, where tornadoes were confirmed at 2246 UTC and UTC. PANEL (C): The last forecast shown was launched at 0100 UTC 7 May 2015, ~45 min before the EF- 3 tornado that impacted southeast parts of Oklahoma City. High probabilities are collocated with tornado occurrence over southeast Oklahoma City, and other parts of the Oklahoma City metropolitan area, where more discrete convection had begun to evolve upscale. Nevertheless, a number of persistent areas of low- level rotation continued to produce high probability values in subsequent forecasts, even as the tornado threat was observed to diminish. This finding underscores the need for the WoF project to investigate techniques that allow better discrimination between non- tornadic and tornadic convective scenarios. b) May 16, 2015 The 16 May 2015 severe weather outbreak produced 10 confirmed tornadoes in the NWS Norman forecast area. The Elmer- Odell- Tipton- Snyder tornado persisted for over one hour along a 40- mile path in southwest Oklahoma, and has been given an EF- 3 rating. A second EF- 1 tornado produced damage across a 12- mile path in Major County (including the town of Cleo Springs) in northwestern Oklahoma. 7

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9 Fig min forecasts of probabilities of 0-2 km vertical vorticity greater than s - 1 (red shading; see color bar) launched at (a) 2130 UTC 16 May 2015, (b) 2200 UTC 16 May 2015, (c) 2230 UTC 16 May 2015, (d) 2300 UTC 16 May 2015, (e) 2330 UTC 16 May 2015, and (f) 0000 UTC 17 May The green contours show ensemble maximum updraft helicity at values of 100, 250, and 400 m 2 s - 2. The gray lines indicate the observed tornado tracks. It is noted that the forecasts launched at 2200 and 2300 UTC 16 May begin approximately 30- min before the Elmer- Odell- Tipton- Snyder and Cleo Springs tornadoes, respectively. HIGHLIGHTS: Panels (a)- (c): The earlier forecasts capture the mixed- mode convection ahead of the dryline, showing numerous vorticity swaths (all with probabilities ~0.6 or greater) associated with tornado- warned and unwarned storms. Particularly at later times in these forecast periods, the vorticity swaths bear a northward displacement in relation to the tornado warning issuance [see in panel (a), for example, the vorticity swath associated with the Elmer- Odell- Tipton- Snyder tornado]. These displacement errors generally decrease with additional storm- scale assimilation. Finally, it is noted in these earlier forecasts (and later ones) that ensemble maximum updraft helicity values greater than 250 m 2 s - 2 were most likely across southwest Oklahoma as compared to northwest Oklahoma thus potentially suggesting a (more) significant event focused on this region, as was observed. Panels (d)- (f): With additional storm- scale assimilation, the measures of rotation become more focused on the two tornado affected areas in Oklahoma, with probabilities exceeding 0.8. Ensemble maximum updraft helicity values are still highest in a southwest- to- northwest corridor between southwest Oklahoma and the Oklahoma City metropolitan area. A number of confirmed, albeit weaker tornadoes, occurred within this corridor, as the storm that produced the Elmer- Odell- Tipton- Snyder moved to the northeast. The observed convection transitioned to a north- south oriented squall line (with diminished tornado threat) as it moved across central Oklahoma in the late evening. 4. Future work This document will be periodically updated as research progresses on other 2015 severe weather events of interest: Date Event 9 April 2015 North Central IL tornado event 26 April 2015 North Central TX tornado event 6 May 2015 Central OK tornado event 8 May 2015 Central OK hail event 16 May 2015 Southwest OK tornado event 23 May 2015 Central OK tornado event Please check back for the most recent results. 9

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