BATS

Overview:

BATS stands for the Bermuda Atlantic Time-series Study. BATS has collected data on the physical, biological, and chemical properties of the ocean every month since 1988. BATS was established to uncover mysteries of the deep ocean by analyzing important hydrographic and biological parameters throughout the water column.

Data Source:

Water column data from BATS (roughly 31N, 64W) can be downloaded from https://bats.bios.asu.edu/bats-data/ The data is stored in in different forms. This converter operates on the ASCII formatted file, often named bats_bottle.txt

The data is huge extending from Oct 1988 to present day. It consists of a long list of information such as Depth, Oxygen, CO2, Nitrate, Phosphate, Silicate, Alkalinity, Organic Carbon, Bacteria, …

Observation Converter:

The obs converter is a program called bats_to_obs and has a namelist by the name &bats_to_obs_nml Namelists start with an ampersand ‘&’ and terminate with a slash ‘/’.

&bats_to_obs_nml
 text_input_file       = "../bats_bottle.txt"
 max_lines             = 68000
 read_starting_at_line = 61
 date_firstcol         = 14
 hourminute_firstcol   = 35
 lat_cols              = 42, 47
 lon_cols              = 51, 56
 vert_cols             = 64, 69
 scalar_obs_cols       = 113, 119,
                         137, 143,
                         145, 151,
                         153, 159,
                         170, 176,
                         178, 184
 obs_uncertainties     = 0.2,
                         0.2,
                         0.2,
                         0.2,
                         0.2,
                         0.2
 obs_out_dir           = '../obs_seq_files',
 debug                 = .true.
/

This namelist provides control over the kind of observations to extract from the file in addition to their uncertainties. In its current form, the observations that are extracted from the data file are:

BATS_OXYGEN, BATS_INORGANIC_CARBON, BATS_ALKALINITY, BATS_NITRATE, BATS_PHOSPHATE, BATS_SILICATE

Contents

Type

Description

text_input_file

character(len=256)

Pathname to the data file: bats_bottle.txt

max_lines

integer

Upper bound on the number of lines in the file that record observations.

read_starting_at_line

integer

Skip the information in the header of the file.

date_firstcol

integer

First column of the YYYYMMDD date code at each line.

hourminute_firstcol

integer

First column of the HHMM time stamp at each line.

lat_cols

integer(2)

First and last columns where latitude is recorded.

lon_cols

integer(2)

First and last columns where longitude is recorded.

vert_cols

integer(2)

First and last columns where depth is recorded.

scalar_obs_cols

integer(:, 2)

ith row of this table should list the first and last columns where the value of the ith observation variable is recorded. Ordering of observation variables is defined by the OTYPE_ORDERING parameter in bats_to_obs.f90.

obs_uncertainties

real(:)

ith entry of this list gives the uncertainty associated with the ith observation variable.

The observation error variance is defind as the square of the product of the obs_uncertainties and the observation value.

obs_out_dir

character(len=256)

Pathname to obs_seq files resulting from the converter.

debug

logical

A switch that makes the converetr prints useful information as it runs.

Climatology:

On top of assimilating real-time data, we often observe the quasi-cyclostationary behavior of the biogeochemical system over the period of one year, and we update MARBL parameters by comparing this observed climatology to a climatology predicted by MARBL. This usually involves running different forms of the ensmeble smoother where the model is re-run using the updated parameters over long periods of times.

To access the observed climatology at BATS, the script bats_climatology.py can be used to generate the climatology by averaging the data over time. The program bats_to_clim_obs can then be executed to generate DART-style observation sequence files using the climatological data. This code also supports Multiple Data Assimilation (MDA) in which the observations are assimilated multiple times with inflated observation error variance.