Enhancing High Impact Weather Forecasts in NGGPS Through Assimilating NYS Mesonet Profiler Observations

Funding Opportunity: NOAA-NWS-NWSPO-2018-2005325

Enhancing high impact weather forecasts in NGGPS through assimilating NYS Mesonet profiler observations

A Proposal Submitted to Round 3 of Research to Operations Initiative NGGPS Competition Priority Area: (a) Data Assimilation

Lead P.I.: Institutional representative: Cheng-Hsuan (Sarah) Lu, Research Associate Ashley Gardner, Research Administrator Atmospheric Sciences Research Center Pre-Award and Compliance Services State University of New York at Albany State University of New York at Albany CESTM Building, 251 Fuller Rd 1400 Washington Ave, MSC 100B Albany, NY 12203 Albany, NY 12222 Phone: (518)-437-8771 Phone: (518) 437-3895 Email: [email protected] Email: [email protected]

Co-P.I: Ryan Torn, Associate Professor Department of Atmos. and Environ. Sciences State University of New York at Albany 1400 Washington Ave Albany NY 12222 Phone: (518)-442-4560 Email: [email protected]

Co-Investigators: Daryl Kleist NOAA/NCEP Environmental Modeling Center, MD; [email protected] Raymond O’Keefe NWS Weather Forecast Office at Albany, NY; [email protected] Michael Evans NWS Weather Forecast Office at Albany, NY; [email protected] William Mccarty NASA Goddard Space Flight Center, MD; will.mccarty@nasa.gov

Funding Requested: September 2018 – August 2020 Investigator Institution Year 1 Year 2 Total Lu-Torn UAlbany $196,511 $186,108 $382,618

Competition: Round 3 of Research to Operations Initiative: NGGPS

Enhancing high impact weather forecasts in NGGPS through assimilating NYS Mesonet profiler observations

Principal Investigator: Cheng-Hsuan (Sarah) Lu, State University of New York at Albany, NY Co-PI: Ryan Torn, State University of New York at Albany, NY Collaborators: Daryl Kleist, NWS/NCEP Environmental Modeling Center, MD Raymond O’Keefe, NWS Weather Forecast Office at Albany, NY Michael Evans, NWS Weather Forecast Office at Albany, NY William Mccarty, NASA Goddard Space Flight Center, MD

Program Priority Area: NGGPS (a) Data Assimilation Requested Budget: $196K for year 1 and $186K for year 2 Project Period: September 2018 – August 2010

ABSTRACT

We propose a two-year research-to-operation (R2O) project to investigate the impact of assimilating a network of ground-based wind lidars on the forecasts of high impact weather events. This project will address the FY2018 NWS Research to Operations Initiative NGGPS competition priorities “(A) Data Assimilation”. This University at Albany (UAlbany) led R2O project will be accomplished through collaboration with scientists at NOAA/NWS National Centers for Environmental Prediction (NCEP) and NASA Goddard Space Flight Center (GSFC) as well as forecasters at NWS Weather Forecast Office at Albany NY. R2O activities proposed in this project support NWS’s objectives to accelerate weather forecasting skills by “effective assimilation of environmental observations at regional scales”.

The NY State Mesonet (NYSM) network is established to help mitigate the vulnerability of New York to severe weather events. It consists of a network of 126 surface meteorological stations strategically deployed across NYS to provide hazardous weather early warning and decision support to the NWS, state emergency managers, and the public. Seventeen of its 126 sites are enhanced to include Doppler wind lidars and microwave radiometers to provide atmospheric profiles of wind, temperature, relative humidity, and other properties in lower atmosphere.

Space-born lidar wind operators have been developed and tested within the Gridpoint Statistical Interpolation (GSI) analysis system at NCEP. This R2O proposal will extend GSI’s wind lidar assimilation capability to assimilate NYSM ground-based lidar observations. Forecast experiments will be conducted to investigate the impact of high-resolution wind profile observations on high-impact weather forecasting. 3

Results from Prior Research

PI Cheng-Hsuan (Sarah) Lu has been the lead developer for global aerosol forecasting system within NOAA Environmental Modeling System (NEMS) at National Centers for Environmental Prediction (NCEP) before joining University at Albany (UAlbany) in 2014. Prior to taking on the NEMS aerosol development, she implemented the Noah (NOAA) land surface model into the Global Forecast System (GFS) and Climate Forecast System (CFS) and conducted extensive land-hydrology tests (both off-line and coupled) to benchmark Noah (NOAA) land surface model (LSM) upgrade with respect to the then operational OSU LSM. She has been one of core developers for NEMS, which will be the foundation upon which Next Generation Global Prediction System (NGGPS) community earth-system modeling system is built.

Her aerosol and land surface modeling work at NCEP demonstrate that she is not only experienced in transitioning external research advances to NCEP operations but also has a solid knowledge of weather forecasting and data assimilation system at NCEP. Since she joined UAlbany, she has been funded by NOAA NESDIS to assimilate Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol retrievals using NCEP Gridpoint Statistical Interpolation (GSI) 3D-Var analysis system. In addition, she has been supported by UAlbany New York State Mesonet (NYSM) project to develop a planetary boundary layer height (PBLH) analysis system based on NCEP’s Real-Time Mesoscale Analysis (RTMA) framework. Her aerosol and PBLH analysis work demonstrate her experiences in enhancing NCEP’s GSI-based data assimilation system.

Statement of Work

1. Introduction

University at Albany, State University of New York (UAlbany), in partnership with the Federal Emergency Management Agency (FEMA), the NYS Division of Homeland Security and Emergency Services, and the National Weather Service (NWS), has begun deployment of an advanced, statewide mesonet (short for mesoscale network) to detect high-impact weather phenomena. High resolution of surface network with an averaged spacing of about 25 km and vertical information on thermodynamics and dynamics fields, particularly within the boundary layer, from 17 profiler sites provides more information on the development and evolution of weather systems and local storms. The deployment of NY network anticipated to improve numerical weather prediction (NWP) skills and awareness as weather events evolve.

We propose a two-year research-to-operation (R2O) project to the Round 3 of Research to Operations Initiative to investigate the impact of assimilating NY’s network of ground-based wind profilers on high impact weather forecasts. These UAlbany-led R2O activities will be accomplished through collaboration with scientists at NOAA/NWS National Centers for Environmental Prediction (NCEP) and NASA Goddard Space Flight Center (GSFC) as well as forecasters at NWS Weather Forecast Office (WFO) in Albany NY.

Scientific Merits

Measurement of the three-dimensional (3D) wind fields have been recognized as the most urgently needed observation type for climate studies as well as numerical weather prediction (WMO, 2004). Moisture and winds in the lower troposphere and PBL structure were cited in a recent National Research Council (NRC) report (2009) as the "most critical observing needs to accurately nowcast severe local storms.” Furthermore, the latest NRC decadal survey (NRC, 2017) recommended “high temporal resolution vertical profiling of the PBL and troposphere at national scale would improve severe weather and air quality forecasting”.

Ground-based 3D scanning Doppler Wind Lidars (DWL) deployed in New York State Mesonet (NYSM) provides local wind observations with high temporal and spatial resolution. This mesoscale network effectively fills the observational gaps in aloft and hourly data, identified in the NRC report (2009). We propose to exploit NYSM ground-based lidar observations to investigate the impact of ground-based DWL measurements on the forecasts of high impact weather events. The results are expected to contribute to the understanding of NWP impact of gap-filling wind profiler data.

Goals and Objectives

The overarching goal is to investigate the impact of a mesoscale network of ground-based DWL on the forecasts of high impact weather events. The tactical approach used to accomplish the goal consists of the following activities: (a) enhancing NCEP’s wind lidar data assimilation capability from single component wind to full vector wind, (b) conducting numerical experiments with and without NYSM 5

DWL wind observations for selected high-impact events, and (c) documenting the forecast impacts of DWL data for these selected cases.

Relevance to Research-to-Operation Initiative Priorities

The exploitation of DWL data for mesoscale applications, proposed in this project, directly responds to the FY2018 NWS Research-to-Operations Initiative Next Generation Global Prediction System (NGGPS) competition priorities “(A) Data Assimilation” through enhancement in NCEP analysis system. The proposed work will contribute towards “advancement of techniques for remotely sensed observations” and “observation impact studies”, identified in this announcement of opportunity as priority work under data assimilation.

This UAlbany-led R2O project will be accomplished through collaboration with scientists at NOAA/NWS/NCEP and NASA/GSFC as well as forecasters at WFO in Albany NY. R2O activities proposed in this project support one of NWS’s objectives to accelerate weather forecasting skills through “effective assimilation of environmental observations at regional scales”.

2. Background

New York State Mesonet (NYSM)

In the past three decades, there is a growth in the number of mesoscale observation networks over various regions of the United States. Observations collected by these mesonet networks are increasingly used to initialize and evaluate forecast models, to improve weather forecasts, and to advance understanding of land–atmosphere interactions and the evolution of meteorological events (Mahmood et al., 2017).

The New York State Mesonet (NYSM, http://nysmesonet.org/) was deployed to help mitigate the vulnerability of NY to severe weather events. It consists of a network of 126 surface meteorological stations strategically deployed across NYS (shown in Figure 1) to provide hazardous weather early warning and decision support to NWS, state emergency managers, and the public. Standard sites measure standard meteorological variables (i.e, pressure, temperature, humidity, wind speed/direction, radiation, precipitation, snow depth) along with soil moisture and temperature at 3 depths. Twenty of the 126 sites have been enhanced with additional snow-related sensors (e.g., snow water equivalent), seventeen sites have been enhanced with 4-component net radiation, soil heat flux, and eddy covariance flux measurements of sensible heat, latent heat, momentum, and CO2, and seventeen sites are enhanced to include lidars, microwave radiometers, and sky imagers to provide atmospheric profiles of wind, temperature, relative humidity, and other properties of the boundary layer. These observations along with an advanced data processing system and high quality data standards make the system one of the most novel and advanced of its kind in the US.

Figure 1 Spatial distribution of NYSM standard sites (green), snow sites (blue), flux sites (red), and profiler sites (yellow).

At seventeen profiler sites shown in Figure 1, specialized instrumentation is installed for profiling the lower atmosphere. These include a DWL measuring 3D aerosol and wind, a multi-frequency microwave profiling radiometer (MWRP) providing vertical profiles of temperature and moisture, and an environmental Sky Imager and Radiometer (eSIR) reporting sky conditions, cloud distributions and properties, spectral solar radiation, and aerosol properties. The DWL deployed by NYSM is Leosphere Windcube 100S, which measures the radial wind speed and reconstructs wind vector using Doppler- Beam-Swinging (DBS) mode. It samples 3D wind every second, and 5-minitue averaged data is processed and archived at NYSM data server. An example for DWL data is shown in Figure 2, illustrating wind observations taken at Buffalo NY, during 3-9 September 2017.

Figure 2. Wind observations taken at Buffalo NY during 3-9 September, 2017.

The radiosonde observation network, with sites distributed several hundred kilometers apart, provides sufficient data on the synoptic scale but lack the necessary spatial and temporal resolution to characterize mesoscale phenomena. Mesoscale network, like NYSM, collects comprehensive 3D measurements, which enable short-range numerical weather prediction, the nowcasting of high impact 7 weather, and chemical weather predictions. Deployment of the NYSM lidar network effectively increases profile observations at NYS from 6 profiles (twice per day from 3 radiosonde stations) to 4000+ profiles (every 5 minute from 17 profiler sites). Note: VAD wind profiles are also available at the WSR-88D sites (BUF, ENX, TYX, OKX) every volume scan so approximately every 5 minutes.

Data Assimilation of Doppler Wind Lidar Data

Doppler wind lidar derives information on air motion from the Doppler shift in backscattered signals from aerosols and/or molecules. It can directly and accurately measure the line-of-sight (LOS) wind. Three-dimensional wind observations from DWL can be essential data sources for reducing analysis errors and improving NWP forecasts. Zhang and Pu (2011) demonstrated that assimilating ground- based wind profiler observations had a significant influence on forecasts of a squall line. Pu et al. (2010) reported positive impact of airborne wind lidar data on numerical simulations of Typhoon Nuri. Kawabata et al. (2014) reported improved forecasts of a heavy rainfall event associated with an isolated mesoscale convective system by assimilating DWL data.

Doppler lidar technology has advanced to the point where space-born wind observations are feasible and potentially leading to major advances in NWP applications and climate research (Baker et al., 2014). The first space-based DWL, called the Atmospheric Dynamics Mission (ADM-Aeolus, Stoffelen et al., 2005), is scheduled for launch by the European Space Agency (ESA) in 2018. Kallen et al. (2010) showed that the satellite observing system is dominated by mass observations while conventional observing system is well balanced in terms of mass and wind observations. Space-borne wind lidar mission such as ADM-Aeolus can reduce the measurement imbalance and thus have a large impact on forecast quality.

Since DWL is a very costly instrument, various Observing Systems Simulation Experiments (OSSEs) were conducted to demonstrate the data impact (Atlas, 1997; Atlas and Emmitt, 2008; Marseille et al., 2008; Masutani et al., 2010; Riishojgaard et al., 2012; Atlas et al., 2015; Ma et al., 2015). Recent global OSSEs have employed the NCEP Gridpoint Statistical Interpolation (GSI) and Global Forecast System (GFS) as the assimilation system and forecast model, respectively, in their lidar impact experiments (Masutani et al., 2010; Riishojgaard et al., 2012; Atlas et al., 2015; Ma et al., 2015). The observation operator for horizontal LOS winds has been developed to assimilate space-borne LOS lidar measurements within the GSI analysis system at NCEP. The lidar wind operator consists an interpolation of the horizontal wind component of the background field to the observation time and location, followed by the projection on the LOS of the lidar. The analysis is then obtained by minimizing the scalar cost function.

Based on a global OSSE study, Ma et al. (2015) obtained a positive impact from simulated space-borne DWL wind observations on NCEP GFS wind and mass forecasts. Figure 3 illustrates the wind lidar impact on tropical wind forecasts as a function of forecast lead time. While the positive impact from simulated DWL wind observations is initially large, the effect tends to decrease rapidly over time at both levels. Figure 4 shows 700-hPa temperature forecast in both the North Hemisphere (NH) and South Hemisphere (SH). Neutral to positive impact is found for temperature when lidar data is incorporated into the GSI analysis system.

Figure 3. The impact of DWL wind measurements from various configuration on 200- and 850-hPa tropical wind forecast, averaged over 40 cases. Error bars represent statistical significance at the 95% level. (Figure 7 in Ma et al., 2015).

Figure 4. 700-hPs RMS forecast error comparison for temperature averaged over the period from 7 Jul to 15 Ag 2015 in the (a) NH and (b) SH. (Figure 11 in Ma et al., 2015).

3. Proposed Research-to-Operation Activities

The goal of this work is to assess the value of ground-based DWL within the NYSM on subsequent high-impact weather forecasts. We plan to study high impact cases in the Northeastern United States that we anticipate to have improved forecasts from assimilating NYSM DWL data. In particular, we plan to focus on: (a) convective cases, where boundary layer observations might help to improve convective initiation and propagation, and (b) these events that have been historically problematic for NWP models in this region, such as lake-effect snow events and mixed precipitation events.

Operations-to-research (O2R) tactical approaches are used to ensure that the project is closely aligned with NWS’s R2O initiatives. These includes: (1) the capability to assimilate NYSM DWL will be added to NCEP GSI analysis system, (2) the forecast experiments will be carried out using NOAA High- Resolution Rapid Refresh (HRRR) data assimilation and modeling system, and (3) the assessment and evaluation will be led by the forecasters at WFO in Albany.

HRRR is a NOAA real-time hourly updated, 3-km resolution, cloud-resolving, and convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. It is developed by NOAA Earth System Research Laboratory (ESRL) and then implemented at NCEP for operational applications. For a variety of reasons, we plan to carry out these tests using NCEP’s HRRR first with an eye toward transitioning the system into the NGGPS global model at the end of the project. First, the new observation information is contained in a very limited geographic area (i.e., New York State); therefore, one would expect that the impact is going to be very limited with respect to the performance metrics used in global model evaluation. As a consequence, it is an inefficient use of computational resources to run a global model when a regional model is sufficient. Moreover, we believe that the biggest impact from these observations will be obtained for certain societally high impact weather cases, which are often separated in time. Given that it typically takes at least 7 days for the global modeling system to come into equilibrium, which means we either need to find a period with numerous high impact weather events, or run the global system for a month with several marginal events. The HRRR system employs a partial cycling methodology; therefore, we can run the system for several high impact weather events without having to worry about spinning up the data assimilation system for long periods of time. Finally, there is an ongoing unification of the data assimilation systems within the NCEP Environmental Modeling Center (EMC) production suite; therefore, we expect that any advancements that we obtain with the HRRR system would translate to the NGGPS global system.

Forecaster Assessment of Performance

The WFO in Albany is tasked with producing forecasts, watches and warnings for high impact weather events in eastern New York and western New England. Despite many upgrades in technology and scientific understanding, significant challenges remain for WFO-Albany forecasters. Many of these challenges are related to small-scale weather features that produce high-impact weather over limited areas. Examples of these types of phenomena include lake effect snow bands, small-scale heavy snow bands associated with large-scale storm systems, severe convective storms, and localized heavy rainfall associated with convection.

The recent implementation of high resolution models has greatly aided WFO-Albany forecasters with the prediction of these systems, as many of these systems are now being explicitly forecast by the models. However, challenges remain, as the forecasts from these high resolution models are not always accurate. As such, any improvement in high resolution numerical modelling accuracy would be of great benefit to their forecast operations.

In this project, forecasters from WFO-Albany office will compare output from HRRR with versus without NYSM data assimilated. A web page will be established for side by side comparisons of key meteorological fields during the course of the events. This web-based tool will enable the forecasters to evaluate the forecast evolution of these events. Meteorological fields to be displayed, suggested by WFO-Albany science and operations officer (SOO), include simulated reflectivity, 1-hour and 3-hour total quantitative precipitation forecasts (QPF), precipitation type, surface or 2 meter wind speed and temperature, and updraft helicity (for summer convective events only).

WFO-Albany has already identified 3 winter events that could be used as case studies for this project. In two of the cases (December 9, 2017 and January 4, 2018), significant snow bands were poorly forecast by operational high-resolution guidance. In another case (January 2, 2018), a short-lived but 10 significant lake effect snow band east of Lake Ontario was under-forecast by operational models. They will continue to look for future potential case studies, in addition to the 3 identified so far.

Proposed Tasks

The proposed tasks will address NGGPS priority area: (a) Data Assimilation. These include:

1) NYSM DWL wind data. Raw wind profiler data will be processed to compile quality- controlled data set. We will compare QC’d wind profiles with the radiosonde data that match the location (both horizontally and vertically) and time of the DWL observations. Observation error for DWL wind profiles will be specified. The first attempt will be based on the regression-based error estimates using independent radiosonde observations. We will also explore the use of the innovation statistics (Dee and da Silva, 1999) that provides the estimates of background and observational error variances.

2) GSI-based analysis system. In preparation for space-borne DWL, lidar wind operator has been developed in GSI to assimilate single component wind profiles. We will revise lidar wind operator to assimilate full vector wind profiles from ground-based DWL. We will also encode NYSM DWL wind profiles into BUFR (Binary Universal Form for the Representation of meteorological data) format, the specific data format used in GSI data ingest.

3) Update HRRR with upgraded GSI. HRRR (currently HRRRv2) uses GSI analysis code for observations pre-processing and calculation of ensemble priors. The experimental HRRRv3 has been run in real-time at ESRL since April 2017 and is targeted for operational implementation at NCEP in May 2018. We will incorporate the new assimilation capability developed in task 2 into HRRRv3. The code development will be conducted at NOAA Research-and-Development (R&D) High-Performance Cluster (HPC), such as Theia, Jet or Gaea.

4) HRRR experiments and evaluation. We will conduct HRRRv3 experiments with and without NYSM DWL wind data at NOAA R&D HPC. The selection of high-impact weather events will be guided by the forecasters at WFO-Albany. Verification of HRRRv3 experiments will be based on statistics metrics currently used at NCEP EMC. Results of HRRRv3 experiments will be processed and displayed at UAlbany website. This web-based tool will be used by the forecasters at WFO-Albany for HRRRv3 performance evaluation.

5) Benchmark report. We will document the new capability in GSI analysis system and summarize the DWL observation impact for these selected high impact weather events.

Synergistic Activities

Major efforts are undertaken at NCEP to unify many of NCEP’s operational NWP suite under the Finite- Volume Cubed-Sphere (FV3) dynamic core and to evolve NGGPS toward a community Earth-system modeling system for global and regional applications. As part of NWS commitment to move toward a National Unified Modeling System, a unification of the verification system based on the community Model Evaluation Tools (MET) developed at National Center for Atmospheric Research (NCAR) is 11 currently ongoing. In addition, there are ongoing development work to transition the operational GSI- based data assimilation system to the Joint Effort for Data Assimilation Integration (JEDI), initiated by Joint Center for Satellite Data Assimilation (JCSDA).

Development work proposed in this proposal will be executed as consistent as possible with the unification strategy under the guidance of the EMC collaborator, Dr. Daryl Kleist. For instance, the choice of GSI code - among current operational system, the parallel pre-operational system, or the experimental system - will follow the guidance from EMC.

4. Project Deliverables and Timetables

An end-to-end work plan is proposed to ensure a robust and timely R2O transition. Timetable to accomplish the proposed tasks during the two-year funding period is presented here.

Tasks 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Task 1. NYSM wind data Process and QC Detemine observation errors Task 2. GSI code development BUFR encoding Revise lidar wind operator

Task 3. HRRRv3 with updated GSI Update HRRR with revised GSI Refine the experimental HRRR Task 4. HRRR experiments and evaluation Baseline runs using operational configuration Experimental runs using updated HRRR Setup Webpage to display HRRR results for WFO Run EMC's vsdb or MET verification Conduct additional case studies

Task 5. Benchmark Report Document DWL observation impact

Project reporting Annual progress reports ■ ■ NCEP site meeting (including R2O annual meeting) 2 trips (TBD) Journal Submission/Review process ■

5. Performance Metrics and Operational Applicability

Evidence-based Verification

The evidence-based evaluation will consist of a combination of statistical evidence and reviews of critical forecast parameters from these high-impact case studies. The former will be accomplished by the utilization of EMC’s verification system and the latter will be accomplished by the forecaster assessment led by WFO-Albany.

Verification at NCEP/EMC, based on verification statistic database (VSDB) output, has been created using one of two codes: grid2obs, which interpolates model data to observation location, and grid2grid, which compares model data against gridded analyses at the model grids. EMC is transitioning VSDB- based verification system toward MET-based unified verification and validation system. We will use either VSDB- or MET-based verification system as per EMC’s collaborator’s guidance to verify HRRRv3 experiments.

Forecaster assessment of performance will be conducted by the forecasters from WFO-Albany. We will establish a web page providing visual inspection of critical forecast parameters during the course of the events. The web-based tool will enable the forecasts to evaluate the forecast performance of the two configuration to determine the impact of NYSM DWL data. The development of web-based tool (such as the layout, the parameters to be displayed) will leverage the expertise experiences from WFO- Albany.

The procedure for operational implementation

NCEP is an operational service center delivering science-based environmental prediction to the Nation and the global community. With so much at stake, any upgrades and improvements will be thoroughly tested and evaluated on a parallel experimental system before being submitted to NCEP Central Operations (NCO) for operational implementation.

The transition-to-operation strategy employed in this project, therefore, consists of two phases: (1) the transition from research to parallel experimental system, and (2) the implementation from the parallel experimental system into operations. The former will be accomplished by the UAlbany-led collaborative efforts outlined here. The latter will be managed by NCEP personnel and is beyond the scope of this project.

To facilitate R2O transition, we will carry out the project as such: . All code and scripts will be managed by the repository systems (Github or vLab) suggested by our NCEP collaborator . The HRRRv3 experiment will be carried out following operational-like configuration using operational workflow or unified workflow, if available . The evaluation of HRRRv3 experiments will be based on the NCEP VSDB- or MET-based performance metrics

6. Management Plan

The project deliverables will be accomplished by the UAlbany team, under the overall coordination of the lead PI (Cheng-Hsuan (Sarah) Lu). The PI will organize monthly or bi-monthly tele-conference meeting to evaluate progress and coordinate activities. Upon completion of each task, the team will communicate the results and status, identify issues and make adjustments, if necessary, prior to initiating subsequent stages. Our team will meet at NCEP two times per year (one trip to attend and provide status update at the annual R2O meeting and the other trip for on-site team meeting). Annual progress reports and final benchmark report will be submitted to the program office.

The NCEP and GSFC external collaborators will provide scientific input and technical guidance. Dr. William Mccarty (NASA/GSFC) has extensive experiences in data assimilation, including assimilating space-borne DWL wind data. He will provide the guidance on incorporating ground-based DWL data into GSI analysis system. Dr. Daryl Kleist (NCEP/EMC) is a key developer for NCEP’s data assimilation system. He will help ensure that the tasks are properly synchronized with EMC’s ongoing efforts while facilitate a smooth R2O transition wherever possible. Raymond O’Keefe (WFO) and Michael Evans (WFO) are the Meteorologist in Charge and SOO at WFO-Albany. Forecasters at their office will evaluate our HRRRv3 experiments and provide performance assessment.

References

Atlas, R., 1997: Atmospheric observation and experiments to assess their usefulness in data assimilation. J. Meteor. Soc. Japan, 75, 111–130. Atlas, R. and G. D. Emmitt, 2008: Review of observing system simulation experiments to evaluate the potential impact of lidar winds. 24th International Laser Radar Conference 2008 (ILRC24), Vol. 2, Curran Associates, 726–729. Atlas, R., and co-authors.,2015: Observing System Simulation Experiments (OSSEs) to Evaluate the Potential Impact of an Optical Autocovariance Wind Lidar (OAWL) on Numerical Weather Prediction, J. Atmos. Oceanic Technol, doi: 10.1175/JTECH-D-15-0038.1_ 2015 Baker, W. E., and co-authors, 2014: Lidar-measured wind profiles. The missing link in the global observing system, Bull. Amer. Met. Soci., 543-564, doi:10.1175/BAMS-D-12-00164.1. Dee, D., and A. da Silva (1999), Maximum-likelihood estimation of forecast and observational error covariance parameters. Part 1: Methodology, Mon. Weather Rev., 127, 1822–1834. Källén, E., D. Tan, C. Cardinali, and P. Berrisford, 2010: Spaceborne Doppler wind lidars Scientific Motivation and Impact Studies for ADM/Aeolus. 33rd Meeting of the Working Group on Space- Based Lidar Winds, Destin, FL, CIRES. [Available online at http://cires.colorado.edu/events/lidarworkshop /LWG/Feb10/Papers.feb10/Kallen.feb10.ppt.] Kawabata, T., H. Iawi, H. Seko, Y. Shoji, K. Saito, S. Ishii, and K. Mizutani, 2014: Cloud-resolving 4D- Var assimilation of Doppler Wind Lidar data on a meso-gamma-scale convective system, Mon. Wea. Rev., 142, 4484-4498, doi:10.1175/MWR-D-13-00362.1 Ma, Z., L.-P. Riishojgaard, M. Masutani, J. S. Woollen, G. D. Emmitt, 2015: Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments, J. Atmos. Oceanic Technol., 32, 478–495. Mahmood, R., R. Boyles, K. Brinson, C. Fiebrich, S. Foster, K. Hubbard, D. Robinson, J. Andresen, and D. Leathers, 2017: Mesonets: Mesoscale weather and climate observations for the United States, Bull. Amer. Met. Soci., 1349-1361, doi:10.1175/BAMS-D-15-00258.1. Marseille, G. J., A. Stoffelen, and J. Barkmeijer, 2008: Impact assessment of prospective space-borne Doppler wind LIDAR observation scenarios, Tellus, Ser. A, 60, 234-248. Masutani, M., and Coauthors, 2010: Observing system simulation experiments at the National Centers for Environmental Prediction, J. Geophys. Res., 115, D07101, doi:10.1029/2009JD012528. National Research Council report, 2009: Observing weather and climate from the ground up: A nationwide network of networks, The National Academies Press, 250 pp., ISBN 978-0-309-12986-2, DOI 10.17226/12540. ---, 2018: Thriving on our changing planet A decadal strategy for earth observation from space, The National Academies Press, 700 pp., ISBN 978-0-309-46757-5, DOI 10.17226/24938 14

Pu, Z., L. Zhang, and G. D. Emmitt, 2010: Impact of airborne Doppler wind lidar data on numerical simulation of a tropical cyclone. Geophys. Res. Lett., 37, L05801, doi:10.1029/ 2009GL041765 Riishojgaard, L. P., Z. Ma, M. Masutani, J. S. Woollen, G. D. Emmitt, S. A. Wood, and S. Greco, 2012: Observation system simulation experiments for a global wind observing sounder. Geophys. Res. Lett., 39, L17805, doi:10.1029/2012GL051814. Stoffelen, A., et al. 2005: The atmospheric dynamics mission for global wind field measurement, Bull. Am. Meteorol. Soc. Am., 86, 73–87. Zhang, L. and Z. Pu, 2011: Four-dimensional assimilation of multitime wind profiles over a single station and numerical simulation of a mesoscale convective system observed during IHOP_2002, Mon. Wea. Rev., 139, 3369-3388, doi: 10.1175/2011MWR3569.1 World Meteorological Organization (WMO), 2004. Third WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, Alpbach, Austria, 9– 12 March 2004. WMO Proceedings TD 1228. 15

Data/Information Sharing Plan

The main deliverables of this R2O project - improved GSI analysis system and updated HRRRv3 system - will be developed in collaboration with NCEP and therefore readily available for their adoption. All scientific advances in forecasting methodologies being made in this project will be made available to the national and international scientific community without restrictions as permitted by applicable law and NOAA regulations.

The environmental data used for this projected is collected by the NYS Mesonet, which funded the purchase and installation of instrumentation and the transfer of data to the operations center at UAlbany. We will comply with the NYSM data sharing plan for the DWL data used in this study.

Output from HRRRv3 simulations will be stored locally on UAlbany computers, for at least one year after publication of any resulting paper. These may not be stored long-term because of the large data volumes associated with them. However, all model configuration files, source code, analysis scripts, processed data, and instrumental comparisons will be stored on backed-up drives for at least five years, ensuring the ability to readily reproduce results.

The data management is PI's responsibility and time associated with the data management will be covered by the PI's salary.