Publication Date:
2020-11-17
Description:
Real-time observations are essential for operational forecasting that in turn can be
used to predict changes of the state of the ocean and its associated biochemical fi elds.
In addition, real-time observations are useful to detect changes in the past with the
shortest delay, to standardize practices in data collection and to exchange data between
remote regions of the ocean and seas. Th e drawback is that real-time observations could
be less accurate than their delayed mode counterparts due to the time constraints for
data dissemination. In situ real-time data are usually decimated to be transmitted in
real time (loss of accuracy and resolution), whereas satellite data are corrected with
approximate algorithms and less ancillary data. Delayed mode quality control analysis
increases the value of the observational data set, fl agging outliers and producing climatological
estimates of the state of the system. Th us real-time data, together with a
modelling system and the climatological estimates, give the appropriate information
for scientifi c studies and applications.
Th e principles of operational science started to develop in the 1940s and 1950s,
based on the combined use of real-time data and modelling systems that can extend
the information from observations in space and time. Operational science is based on
a sound knowledge of the dynamics and processes for the space/timescales of interest
and operational meteorology and oceanography have started to implement these principles
to weather and ocean forecasting activities.
In the past 20 years, operational meteorology has become a reality with a network of
in situ and satellite observations that has made the weather forecast capable of extending
the theoretical limit of predictability of the atmosphere (only one-two days theoretically,
now forecasts are useful for more than fi ve days on average). Today meteorological
observations are mainly used in their assimilated form even if observations are still
collected for specifi c process-oriented studies. Recently the meteorological re-analysis
projects (Gibson et al., 1997; Kalnay et al., 1996) have released a wealth of data to be
understood and analysed. Th ese data sets are coherent and approximately continuous
(daily), fi lling the observational gaps in space and time with a dynamical interpolation
scheme. Th e model and the real-time observations are fused in one best estimate of the
state of the system by data-assimilation techniques that have been developed to a great
degree of sophistication in recent years (Lorenc, 2002). Th e re-analysis data are now
forming the basic reference data set to understand climate variability in the atmosphere
and upper oceans.
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Dynamical interpolation/extrapolation of observational data for operational
forecasting in the ocean began to be investigated at the beginning of the 1980s and the
fi rst successful forecasts were carried out in the open ocean (Robinson and Leslie, 1985).
Th ese exercises required real-time data that were initially collected with rapid ship surveys
realizing adaptive sampling schemes and collecting a combination of traditional
recoverable and expendable instruments (CTD, XBTs). At the same time but in a totally
independent way, shelf scale and coastal real-time data from moored and drifting sensors
such as meteorological buoys and sea-level stations started to be used for shelf scale
storm surge operational forecasting (Prandle, 2002). Operational oceanography is now
building on this experience and considers real-time measurements from opportunity
platforms and satellites in a manner very similar to operational meteorology.
Th is chapter aims to show the use of real-time observations in a state-of-the-art
ocean-predicting system realized in the Mediterranean. We discuss the pre-processing
schemes required to properly assimilate the observations into an operational nowcasting/
forecasting system, elucidate the role and impact of diff erent observations in the
assimilation system and show the use of real-time data to evaluate quality of the modelling
system.
We start with the description of the Mediterranean Forecasting System (MFS)
real-time observing system and pre-processing quality control in Section 20.2, we then
describe the modelling and assimilation system in relation to the impact of diff erent
real-time observations in Section 20.3. In Section 20.4 we evaluate the consistency,
quality and accuracy of the forecasting system using model-data intercomparison and
Section 20.5 offers conclusions
Description:
Published
Description:
4A. Oceanografia e clima
Description:
open
Keywords:
ocean data assimiliation,
;
Mediterranean case
;
01. Atmosphere::01.02. Ionosphere::01.02.06. Instruments and techniques
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
book chapter
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