Wednesday, December 12, 2012 2:00 p.m. - 3:00 p.m. Auditorium, NOAA Center for Weather and Climate Prediction,5830 University Research Court, College Park, MD
The NPP Suomi satellite has 3 sensors, ATMS, CrIS, and OMPS which are of high interest
for assimilation into the Navy's 4-Dimensional Variational (4D-Var) assimilation system,
NAVDAS-AR. This presentation will primarily address the work done with the ATMS sensor,
and will show some of the early work with OMPS; however, the assimilation of CrIS will
be saved for a future date. The core of the Navy's global forecast system is the 42-level
NOGAPS model, the follow-on system is the 50-level NAVGEM model both with a model
top of 0.04 hPa and both are accompanied by the 4D-Var system NAVDAS-AR. The system
currently assimilates radiances from microwave (AMSU-A, MHS, SSMIS) and infrared
satellite sounders (AIRS and IASI) in addition to the bending angle from GNSS-RO.
The current NAVDAS-AR system can compute bias corrections for satellite radiances
from either an offline Harris-Kelley type approach, or using a variational bias
correction (varBC). The experiments using the 42-level NOGAPS model use a Harris-Kelly
bias correction, while NAVGEM experiments use the varBC. The assimilation of ATMS
produces stable innovations (observation minus background) over time, with biases
of similar magnitude to those of heritage MW sounders. The current observational
system has good data coverage in space and time from IR/MW sounders; thus a forecast
metric such as 500 hPa anomaly correlation against self-analysis does not show
significant impact from the addition of ATMS assimilation. However, the examination
of adjoint-based observation impact using a 24-hour global moist total energy error
norm shows consistent positive impact from ATMS with very similar observation impact
as seen in AMSU-A.
Wednesday, November 14, 2012 2:00 p.m. - 3:00 p.m. Auditorium, NOAA Center for Weather and Climate Prediction,5830 University Research Court, College Park, MD
Spaceborne passive microwave observations from sensors such as AMSU and
ATMS provide temperature and moisture sounding in both clear air and
non-precipitating clouds, along with indirect determination of rain
rate over heavier precipitating clouds. Improvements in mesoscale
forecasting could be anticipated if such observations were made
with higher spatial and temporal resolution, as possible, for example,
from either geostationary orbit or from a fleet of LEO satellites.
While both of these mission concepts are being studied, there is an
associated need to understand how data from such new observation
systems might be used within radiance assimilation schemes. We
discuss in this talk progress in extended Kalman filtering and
radiative transfer modeling applied to the problem of how to optimally
use high time-space resolution passive microwave imagery, and
specifically, the potential for ?precipitation locking? of NWP
models onto passive microwave data. It is suggested from ab
initio precipitation locking studies, although not yet proven,
that time and space scales of ~15 minutes and ~15 km are needed
to maintain small enough model errors in all fields so as to
keep NWP models locked under conditions of mesoscale convection.
The concept of locking will be illustrated through geostationary
microwave observation system simulation experiments. New
developments in fast Jacobian RT modeling and CubeSat based
sensor concepts for facilitating high temporal/spatial measurements
will also be discussed.
Friday, November 2, 2012 2:00 p.m. - 3:00 p.m. Conference Center, NOAA Center for Weather and Climate Prediction,5830 University Research Court, College Park, MD
A conundrum of predictability research is that while the prediction
of flow dependent error distributions is one of its main foci,
chaos fundamentally hides flow dependent forecast error distributions
from empirical observation. Empirical estimation of such error
distributions requires that one obtain a large sample of error
realizations given the same flow and the same observational network.
However, chaotic elements of the flow and the observing network make
it practically impossible to observe and collect the conditioned
sample of errors required to empirically define such distributions
and their variance. These variances are ?hidden?. Here, an exposition
of the problem is developed from an ensemble Kalman filter data
assimilation system applied to a 10 variable non-linear chaotic
model and 25,000 replicate models. The output from this system
motivates a new analytical model for the distribution of true
error variances given an imperfect ensemble variance. This model
is defined by 6 parameters that also determine the optimal weights
for the static and flow dependent parts of Hybrid error variance
models. Six new equations enable these hidden parameters to
be accurately estimated from a long time series of (innovation,
ensemble variance) data pairs. This new-found ability to estimate
hidden parameters provides new tools for assessing the quality of
ensemble forecasts, tuning Hybrid error variance models and for
post-processing ensemble forecasts. Preliminary results from attempts
to use the theory to speed the tuning of Hybrid data assimilation
schemes will also be presented.
K. Wargan
NASA/Goddard Space Flight Center/Global Modeling and Assimilation Office
Date
Tuesday, October 16, 2012 2:00 p.m. - 3:00 p.m. Conference Center, NOAA Center for Weather and Climate Prediction,5830 University Research Court, College Park, MD
Assimilation of ozone data is an important and challenging component of atmospheric
data analysis for operational and research purposes. In its operational analyses,
the Global Modeling and Assimilation Office uses total column ozone data from the
Ozone Monitoring Instrument (OMI) on the EOS Aura satellite and partial ozone columns
from Solar Backscatter Ultraviolet (SBUV) sounders on NOAA platforms. These
measurements are characterized by a relatively coarse vertical resolution of SBUV,
particularly below the ozone peak, and coverage limited to the sunlit atmosphere.
In this presentation we will discuss some recent changes introduced to ozone
assimilation in the GEOS-5 Data Assimilation System (GEOS-DAS). We will describe
a state-dependent approach to the background error covariance modeling and show how
this approach leads to significant improvements in the representation of ozone
in the Upper Troposphere and Lower Stratosphere. The second part of the talk
will be devoted to assimilation of the Microwave Limb Sounder (MLS) data.
Unlike the SBUV instruments, MLS can measure chemical composition and temperature
in both day and night and possesses a much higher vertical resolution. We will
show how MLS data can improve GEOS-DAS analyses and discuss our current work
on direct assimilation of MLS radiance data in spectral bands sensitive to ozone and temperature.
Thursday, September 13, 2012 2:00 p.m. - 3:00 p.m. Auditorium, NOAA Center for Weather and Climate Prediction,5830 University Research Court, College Park, MD
A review of the history of the Joint Center for Satellite Data (JCSDA)
is presented within the context of the ongoing successful transformation
of the weather forecast process. Today, weather forecasts are becoming
more accurate, with extreme weather events now predicted 4, 5, 6 and even
7+ days in advance in some cases. These improvements are driven in large
part by improved numerical models, working off a global observing system,
increasingly based on a wide range of satellite systems. The JCSDA
represents a collaborative partnership among NASA, NOAA, Air Force and
the Navy brought together to address the mission "to accelerate and
improve the quantitative use of research and operational satellite data
in weather, ocean, climate and environmental analysis and prediction models."
The accomplishments of the JCSDA along with the current
challenges/opportunities will be discussed, with an emphasis placed on
the ongoing efforts to accelerate the transition of the new research
and operational observing capabilities (advanced microwave,
hyperspectral infrared, GPSRO, GOES-R) into the operational
numerical prediction system. The presentation will conclude with
a summary of the current prioritized efforts required to insure the
rapid assessment and operational implementation of the JPSS and
GOES-R sensors and upcoming research satellite missions as these
systems come on line in the latter half of this decade.
The Suomi National Polar-orbiting Partnership (NPP) Advanced
Technology Microwave Sounder (ATMS) provides 22 channels for
probing atmospheric temperature and water vapor under all weather
conditions. After intensive cal/val studies, the ATMS TDR data
quality is similar to the Advanced Microwave Sounding Unit (AMSU)
and Microwave Humidity Sounder (MHS). The global O-B distributions
of ATMS temperature sounding channels are fairly uniform, which is
a desirable characteristic for NWP applications. For quality
control of clouds and precipitation affected radiances, the AMSU-A
cloud liquid algorithm has been refined for ATMS applications. A
new approach is developed for ATMS TDR to SDR conversion using the
NPP pitch-over maneuver data. Using ATMS SDR data, the O-B
displays less scan-angle dependence. On board the DMSP satellite,
the Special Sensor Microwave Imager and Sounder (SSMIS) provides
24 channels for imaging and sounding the earth atmosphere. The
radiance anomalies of the lower atmospheric sounding (LAS)
channels were initially detected by analyzing NWP O-B and found to
be associated with the radiation emitted by the antenna reflector
and the solar contamination on the calibration targets. Algorithms
have been developed to correct these anomalies. After the
correction, impacts of SSMIS data on NWP medium-range forecast
skills are demonstrated.
One of the many scientific challenges in the field of numerical
weather prediction is to extract useful information on the
atmospheric boundary layer using observations from remote sensing
microwave instruments such as AMSU-A, AMSU-B/MHS or SSMIS. These
data contribute increasingly to improve short to medium range
forecasts, but also to improve re-analyses. Better use of these
data often requires appropriate representation of the surface in
the models, in both emissivity and temperature. This is achieved
over sea, and satellite data have a tremendous impact on the
atmospheric analyses over oceans. Over land, the surface
emissivity is highly variable and may cause biases in the forward
model if its temporal and spatial variability are not well taken
into account. In such a situation, the model cannot produce
realistic simulations of observations sensitive to the surface and
may reject useful observations, including those not sensitive to
the surface. This case concerns in particular the land and sea ice
surfaces for which the surface emissivity is particularly
challenging to model. During the seminar, some of the work carried
out at Météo-France for a better description of the emissivity of
land and sea ice surfaces will be summarized. The methodology used
for estimating the emissivity in the model and its impact on the
performance of the radiative transfer model will be presented. The
impact of a proper modeling of the land and sea ice emissivity, in
terms of forecast skill and quality of analyses, will be
discussed.
The presentation will summarize some of the key updates of the
ECMWF model and data assimilation systems over the past 2 years,
after a brief introduction of ECMWF and its forecasting framework.
Three examples of system upgrades will be given, namely the
introduction of the ensemble of data assimilations, the revision
of microwave sounder observation errors, and the new prognostic
cloud scheme. Their respective impact on analysis and forecast
skill will be demonstrated as well as the difficult trade-off
between objective system improvements and positive impact on
skill. Finally, the importance of observation monitoring in data
assimilation will be highlighted with two examples.
The Suomi NPP satellite was successfully launched on October 28, 2011 and carries the following five sensors:
Visible/Infrared Imager Radiometer Suite (VIIRS) providing advanced imaging and radiometric capabilities.
Cross-track Infrared Sounder (CrIS) providing improved high vertical resolution atmospheric temperature and moisture information.
Advanced Technology Microwave Sounder (ATMS) providing atmospheric temperature and moisture in all weather conditions.
Ozone Mapping and Profiler Suite (OMPS) providing improved vertical and horizontal measurements of the distribution of ozone in the Earth's atmosphere.
Clouds and the Earth's Radiant Energy System (CERES) sensor providing precise, calibrated global measurements of the earth's radiation budget
The Suomi NPP mission is the bridge between NOAA's Polar
Operational Environmental Satellite (POES) and NASA's Earth
Observing System satellites to the next-generation Joint Polar
Satellite System, or JPSS, which NOAA will operate. NPP will
provide on-orbit testing and validation of sensors,
algorithms, ground-based operations, and data processing
systems that will be used in the operational JPSS mission. The
first JPSS spacecraft will be launched into the afternoon
orbit by the middle of the decade to provide significantly
improved operational capabilities and benefits; the last
satellite in the JPSS series is expected to continue
operations until about 2037. The JPSS program has an active
program of user engagement to maximize the benefits of NPP and
JPSS for critical products and services such as weather
forecasting.
At the seminar, an overview of the JPSS program, some early
results from each instrument, and user engagement will be
presented.
The goal of the present study is to develop a method to
assimilate Microwave Imager (MWI) brightness temperatures (TBs)
into Cloud-Resolving Models (CRMs). To address the non-linear
relationship of TBs to the state variables of CRM and the flow-
dependency of the CRM forecast error covariance, we adopted an
Ensemble-based variational data assimilation method (EnVA). In
this presentation, I will report our recent studies on the
following problems in EnVA:
Large-scale displacement errors of rainy areas between the observations and the CRM forecasts;
Serious sampling errors of cloud-physical variables because they were confined to rainy areas.
In order to solve the displacement error problem, we developed the EnVA that used Ensemble forecasts with displacement error correction. In order to alleviate the sampling error, we are introducing the following ideas to the EnVA:
Use of ensemble forecasts at neighboring grid points;
Classification of CRM variables and assumption of zero cross correlation between different classes.