An Assessment of Data from the Advanced Technology Microwave Sounder at the Met Office

An appraisal of the Advanced Technology Microwave Sounder (ATMS) for use in numerical weather prediction (NWP) is presented, including an assessment of the data quality, the impact on Met Office global forecasts in preoperational trials, and a summary of performance over a period of 17 months operational use. After remapping, the noise performance (NEΔT) of the tropospheric temperature sounding channels is evaluated to be approximately 0.1 K, comparing favourably with AMSU-A. However, the noise is not random, differences between observations and simulations based on short-range forecast fields show a spurious striping effect, due to 1/f noise in the receiver. The amplitude of this signal is several tenths of a Kelvin, potentially a concern for NWP applications. In preoperational tests, adding ATMS data to a full Met Office system already exploiting data from four microwave sounders improves southern hemisphere mean sea level pressure forecasts in the 2- to 5-day range by 1-2%. In operational use, where data from five other microwave sounders is assimilated, forecast impact is typically between −0.05 and −0.1 J/kg (3.4% of total mean impact per day over the period 1 April to 31 July 2013). This suggests benefits beyond redundancy, associated with reducing already small analysis errors.

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The impact of upper tropospheric temperature change on tropical cyclone

10.5194/egusphere-egu21-13735 ◽

2021 ◽

Author(s):

Nawo Eguchi ◽

Kenta Kobayashi ◽

Kosuke Ito ◽

Tomoe Nasuno

Keyword(s):

Tropical Cyclone ◽

Lower Stratosphere ◽

Intensity Change ◽

Tropospheric Temperature ◽

Upward Motion ◽

Sea Level Pressure ◽

Upper Troposphere ◽

Ocean Coupling ◽

Cold Simulation ◽

The Impact

We evaluate the impact of temperature at the upper troposphere and lower stratosphere (UTLS) on the tropical cyclone (TC) generation and its development by using the nonhydrostatic atmosphere-ocean coupling axisymmetric numerical model [Rotunno and Emanuel, 1987; Ito et al., 2010]. In the case of cold simulation at UTLS, the maximum wind and the minimum sea level pressure are increased and decreased than the control run, respectively. The magnitude of intensity change is the approximately 4 times larger than the change estimated from the MPIs (Maximum Potential Intensity [Bister and Emanuel,1998; Holland, 1997]). Further, during the development phase, the cold air mass intrudes to the middle troposphere from the upper troposphere at the center of TC, which is not seen in the warm case, leading the atmosphere unstable and enhanced the upward motion and then the TC got stronger.

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Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

Remote Sensing ◽

10.3390/rs12071147 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1147

Author(s):

Yanhui Xie ◽

Min Chen ◽

Jiancheng Shi ◽

Shuiyong Fan ◽

Jing He ◽

...

Keyword(s):

Heavy Rainfall ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

Advanced Technology ◽

Added Value ◽

Rainfall Forecast ◽

Hourly Rainfall ◽

Radiance Data ◽

The Impact

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

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The Impact of Low Latency Satellite Sounder Observations on Local Severe Storm Forecasts in Regional NWP

Sensors ◽

10.3390/s20030650 ◽

2020 ◽

Vol 20 (3) ◽

pp. 650 ◽

Cited By ~ 1

Author(s):

Pei Wang ◽

Jun Li ◽

Timothy J. Schmit

Keyword(s):

Positive Impact ◽

Weather Prediction ◽

Horizontal Resolution ◽

Advanced Technology ◽

Low Latency ◽

Telecommunication System ◽

Global Telecommunication System ◽

Direct Broadcast ◽

The Impact ◽

Data Latency

The forecasts of local severe storms (LSS) are highly dependent on how well the pre-convection environment is characterized in the numerical weather prediction (NWP) model analysis. The usefulness of the forecasts is highly dependent on how frequently the forecast is updated. Therefore, the data latency is critical for assimilation into regional NWP models for it to be able to assimilate more data within the data cut-off window. These low latency data can be obtained through direct broadcast sites and direct receiving systems. Observing system experiments (OSE) were performed to study the impact of data latency on the LSS forecasts. The experiments assimilated all existing observations including conventional data (from the global telecommunication system, GTS) and satellite sounder radiance data (AMSU-A (The Advanced Microwave Sounding Unit-A), ATMS (Advanced Technology Microwave Sounder), CrIS (Cross-track Infrared Sounder), and IASI (Infrared Atmospheric Sounding Interferometer)). They were carried out in a nested domain with a horizontal resolution of 9 km and 3 km in the weather research and forecasting (WRF) model. The forecast quality scores of the LSS precipitation forecasts were calculated and compared with different data cut-off widows to evaluate the impact of data latency. The results showed that low latency can lead to an improved and positive impact on precipitation and other forecasts, which indicates the potential application of LEO direct broadcast (DB) data in a high-resolution regional NWP for LSS forecasts.

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Improving numerical simulation of typhoon LEKIMA(2019) through assimilating ATMS radiance data

10.5194/egusphere-egu2020-6256 ◽

2020 ◽

Author(s):

Lei Zhang ◽

Baode Chen

Keyword(s):

Numerical Simulation ◽

Data Assimilation ◽

Inner Core ◽

Weather Prediction ◽

Advanced Technology ◽

Limited Area ◽

Numerical Weather ◽

Satellite Radiance Data ◽

Radiance Data ◽

The Impact

Lacking of high-resolution observations over oceans is one of the major problems for the numerical simulation of the tropical cyclones (TC), especially for the tropical cyclone inner-core structure&#8217;s simulation. Satellite observations plays an important role in improving the forecast skills of numerical weather prediction (NWP) systems. Many studies have suggested that the assimilation of satellite radiance data can substantially improve the numerical weather forecast skills for global model. However, the performance of satellite radiance data assimilation in limited-area modeling systems is still controversial.This study attempts to investigate the impact of assimilation of the Advanced Technology Microwave Sounder (ATMS) satellite radiances data and its role to improve the model initial condition and forecast of typhoon LEKIMA(2019) using a regional mesoscale model. In this study, detailed analysis of the data impact will be presented, also the results from different data assimilation methods and different data usage schemes will be discussed.

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What Polarimetric Weather Radars Offer to Cloud Modelers: Forward Radar Operators and Microphysical/Thermodynamic Retrievals

Atmosphere ◽

10.3390/atmos11040362 ◽

2020 ◽

Vol 11 (4) ◽

pp. 362 ◽

Cited By ~ 1

Author(s):

Alexander V. Ryzhkov ◽

Jeffrey Snyder ◽

Jacob T. Carlin ◽

Alexander Khain ◽

Mark Pinsky

Keyword(s):

Cloud Model ◽

Weather Prediction ◽

Primary Source ◽

Polarimetric Radar ◽

Microphysical Process ◽

Weather Radars ◽

Cloud Models ◽

Cloud Modeling ◽

Bin Microphysics ◽

The Impact

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

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Environmental Management, Green Innovation, and Social–Open Innovation

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc7010089 ◽

2021 ◽

Vol 7 (1) ◽

pp. 89

Author(s):

Pham Thu Huong ◽

Jacob Cherian ◽

Nguyen Thi Hien ◽

Muhammad Safdar Sial ◽

Sarminah Samad ◽

...

Keyword(s):

Environmental Management ◽

Stock Exchange ◽

Advanced Technology ◽

Green Innovation ◽

Social Image ◽

Whole Process ◽

Corporate Environmental Management ◽

The Impact ◽

The Relationship

The present study aims to determine the impact of green innovation (GI) on the overall performance of an organization while keeping the variable of environmental management (EM) as a moderator. We used a dataset consisting of four data years, from 2014 to 2017, of A-share companies listed on the Shanghai Stock Exchange (SSE). The concept of green innovation refers to the use of advancements in technology that enable savings in energy, along with the recycling of waste material. When advanced technology is utilized in the production process, the products are referred to as green products and the whole process of adopting such technologies and product design is referred to as “Corporate Environmental Management”. Such innovations improve the overall financial performance of companies as it enables them to improve their social image by reducing their carbon footprint and ensures their long-term sustainability. The main issue is the limited focus and attention given to the topic, from the perspective of companies. This research focuses on the impact of green innovation and the importance of environmental management for the sustainability of companies. Our findings suggest that the relationship between green innovation and the performance of the company is positive and verifies the existence of moderating effects of environmental management on the relationship between green innovation and firm performance. Implications are given to academia and practitioners.

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Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

Geoscientific Model Development ◽

10.5194/gmd-12-3939-2019 ◽

2019 ◽

Vol 12 (9) ◽

pp. 3939-3954

Author(s):

Frederik Kurzrock ◽

Hannah Nguyen ◽

Jerome Sauer ◽

Fabrice Chane Ming ◽

Sylvain Cros ◽

...

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Cloud Water ◽

Specific Humidity ◽

Reunion Island ◽

Short Term ◽

Water Path ◽

Réunion Island ◽

Cloud Water Path ◽

The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

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On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

Monthly Weather Review ◽

10.1175/mwr-d-17-0028.1 ◽

2018 ◽

Vol 146 (2) ◽

pp. 599-622 ◽

Cited By ~ 7

Author(s):

David D. Flagg ◽

James D. Doyle ◽

Teddy R. Holt ◽

Daniel P. Tyndall ◽

Clark M. Amerault ◽

...

Keyword(s):

Boundary Layer ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Research Laboratory ◽

Weather Prediction ◽

Ocean Model ◽

Naval Research ◽

Unmanned Aerial System ◽

Numerical Weather ◽

The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

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Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0243.1 ◽

2015 ◽

Vol 54 (8) ◽

pp. 1809-1825 ◽

Cited By ~ 18

Author(s):

Yaodeng Chen ◽

Hongli Wang ◽

Jinzhong Min ◽

Xiang-Yu Huang ◽

Patrick Minnis ◽

...

Keyword(s):

Data Assimilation ◽

Liquid Water ◽

Weather Prediction ◽

Wrf Model ◽

Liquid Water Path ◽

Cloud Liquid Water ◽

Water Path ◽

Ice Water Path ◽

Ice Water ◽

The Impact

AbstractAnalysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

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The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA)

Remote Sensing ◽

10.3390/rs13030426 ◽

2021 ◽

Vol 13 (3) ◽

pp. 426

Author(s):

Zheng Qi Wang ◽

Roger Randriamampianina

Keyword(s):

Weather Prediction ◽

Deterministic System ◽

Observation Error ◽

Key Factors ◽

Background Error ◽

Current Configuration ◽

Error Covariance ◽

Regional Reanalysis ◽

The Impact ◽

European Regional

The assimilation of microwave and infrared (IR) radiance satellite observations within numerical weather prediction (NWP) models have been an important component in the effort of improving the accuracy of analysis and forecast. Such capabilities were implemented during the development of the high-resolution Copernicus European Regional Reanalysis (CERRA), funded by the Copernicus Climate Change Services (C3S). The CERRA system couples the deterministic system with the ensemble data assimilation to provide periodic updates of the background error covariance matrix. Several key factors for the assimilation of radiances were investigated, including appropriate use of variational bias correction (VARBC), surface-sensitive AMSU-A observations and observation error correlation. Twenty-one-day impact studies during the summer and winter seasons were conducted. Generally, the assimilation of radiances has a small impact on the analysis, while greater impacts are observed on short-range (12 and 24-h) forecasts with an error reduction of 1–2% for the mid and high troposphere. Although, the current configuration provided less accurate forecasts from 09 and 18 UTC analysis times. With the increased thinning distances and the rejection of IASI observation over land, the errors in the analyses and 3 h forecasts on geopotential height were reduced up to 2%.

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